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 machine type communications in 5g physical and mac layer solutions"
  Abstract: MTC are expected to play an essential role within future 5G systems. In the FP7 project METIS, MTC has been further classified into mMTC and uMTC. While mMTC is about wireless connectivity to tens of billions of machinetype terminals, uMTC is about availability, low latency, and high reliability. The main challenge in mMTC is scalable and efficient connectivity for a massive number of devices sending very short packets, which is not done adequately in cellular systems designed for human-type communications. Furthermore, mMTC solutions need to enable wide area coverage and deep indoor penetration while having low cost and being energy-efficient. In this article, we introduce the PHY and MAC layer solutions developed within METIS to address this challenge.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal modeling for massive and sporadic access"):
  with members:
    - "tradeoff between delay and physical layer security in wireless networks"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"
    - "random access analysis for massive iot networks under a new spatio temporal model a stochastic geometry approach"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "massive machine type communication with data aggregation and resource scheduling"
    - "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics"
    - "spatial spectrum and energy efficiency of random cellular networks"

Connected through hyperedge HE-2 (induced by source paper "phase transition analysis for covariance based massive random access with massive mimo"):
  with members:
    - "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "massive mimo unsourced random access"
    - "joint active user detection and channel estimation in massive access systems exploiting reed muller sequences"
    - "multi cell sparse activity detection for massive random access massive mimo versus cooperative mimo"
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "sparse activity detection for massive connectivity"

Connected through hyperedge HE-3 (induced by source paper "on throughput of compressive random access for one short message delivery in iot"):
  with members:
    - "massive mimo has unlimited capacity"
    - "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"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "random pilot and data access in massive mimo for machine type communications"
    - "massive mimo for wireless sensing with a coherent multiple access channel"
    - "asynchronous code division random access using convex optimization"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-4 (induced by source paper "deep energy autoencoder for noncoherent multicarrier mu simo systems"):
  with members:
    - "channel agnostic end to end learning based communication systems with conditional gan"
    - "end to end learning of communications systems without a channel model"
    - "an introduction to deep learning for the physical layer"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "deep learning in neural networks"
    - "energy based modulation for noncoherent massive simo systems"
    - "performance analysis of energy detection based massive simo"

Connected through hyperedge HE-5 (induced by source paper "bayesian receiver design for grant free noma with message passing based structured signal estimation"):
  with members:
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "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 non orthogonal multiple access for cellular iot potentials and limitations"
    - "five disruptive technology directions for 5g"
    - "expectation propagation for approximate bayesian inference"
    - "a generalized mean field algorithm for variational inference in exponential families"

Connected through hyperedge HE-6 (induced by source paper "optimizing non orthogonal multiple access in random access networks"):
  with members:
    - "a survey of rate optimal power domain noma schemes for enabling technologies of future wireless networks"
    - "a game theoretic approach for noma aloha"
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "noma based random access with multichannel aloha"

Connected through hyperedge HE-7 (induced by source paper "multichannel aloha with exploration phase"):
  with members:
    - "layered non orthogonal random access with sic and transmit diversity for reliable transmissions"
    - "noma based random access with multichannel aloha"
    - "coded random access applying codes on graphs to design random access protocols"
    - "asynchronous code division random access using convex optimization"

Connected through hyperedge HE-8 (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"

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)