human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "massive connectivity with massive mimo part ii achievable rate characterization"
  Abstract: This two-part paper aims to quantify the cost of device activity detection in an uplink massive connectivity scenario with a large number of devices but device activities are sporadic. Part I of this paper shows that in an asymptotic massive multiple-input multiple-output (MIMO) regime, device activity detection can always be made perfect. Part II of this paper subsequently shows that despite the perfect device activity detection, there is nevertheless significant cost due to device detection in terms of overall achievable rate, because of the fact that nonorthogonal pilot sequences have to be used in order to accommodate the large number of potential devices, resulting in significantly larger channel estimation error as compared to conventional massive MIMO systems with orthogonal pilots. Specifically, this paper characterizes each active user's achievable rate using random matrix theory under either maximal-ratio combining (MRC) or minimum mean-squared error (MMSE) receive beamforming at the base station (BS), assuming the statistics of their estimated channels as derived in Part I. The characterization of user rate also allows the optimization of pilot sequences length. Moreover, in contrast to the conventional massive MIMO system, the MMSE beamforming is shown to achieve much higher rate than the MRC beamforming for the massive connectivity scenario under consideration. Finally, this paper illustrates the necessity of user scheduling for rate maximization when the number of active users is larger than the number of antennas at the BS.

Connected through hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "the roadmap to 6g ai empowered wireless networks"
    - "improved scaling law for activity detection in massive mimo systems"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"

Connected through hyperedge HE-2 (induced by source paper "random access for massive machine type communications"):
  with members:
    - "beamwidth control for noma in hybrid mmwave communication systems"
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "on the performance gain of noma over oma in uplink single cell systems"
    - "multi beam noma for hybrid mmwave systems"
    - "joint pilot and payload power control for uplink mimo noma with mrc sic receivers"
    - "reliability based windowed decoding for spatially coupled ldpc codes"
    - "short packet downlink transmission with non orthogonal multiple access"
    - "a multi beam noma framework for hybrid mmwave systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo for internet of things iot connectivity"):
  with members:
    - "massive mimo unsourced random access"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "non stationarities in extra large scale massive mimo"
    - "ubiquitous cell free massive mimo communications"
    - "sparcs for unsourced random access"
    - "management of network slicing in 5g radio access networks functional framework and information models"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"

Connected through hyperedge HE-4 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"

Connected through hyperedge HE-5 (induced by source paper "contextual bandit learning for machine type communications in the null space of multi antenna systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "sleeping multi armed bandit learning for fast uplink grant allocation in machine type communications"
    - "a directed information learning framework for event driven m2m traffic prediction"
    - "fast uplink grant for machine type communications challenges and opportunities"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "random access schemes in wireless systems with correlated user activity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "distributed learning for low latency machine type communication in a massive internet of things"

Connected through hyperedge HE-6 (induced by source paper "fixed symbol aided random access scheme for machine to machine communications"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "sparse message passing based preamble estimation for crowded m2m communications"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "irregular repetition slotted aloha over the rayleigh block fading channel with capture"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"

Connected through hyperedge HE-7 (induced by source paper "dnn aided block sparse bayesian learning for user activity detection and channel estimation in grant free non orthogonal random access"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "fixed symbol aided random access scheme for machine to machine communications"
    - "deep learning methods for improved decoding of linear codes"
    - "asymptotically optimal estimation algorithm for the sparse signal with arbitrary distributions"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"
    - "coded slotted aloha a graph based method for uncoordinated multiple access"

Connected through hyperedge HE-8 (induced by source paper "on the fundamental limits of mimo massive access communication"):
  with members:
    - "fundamental limits of many user mac with finite payloads and fading"
    - "massive mimo unsourced random access"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "fundamental limits of low density spreading noma with fading"
    - "capacity of gaussian many access channels"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)