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: "sparse activity detection for massive connectivity"
  Abstract: This paper considers the massive connectivity application in which a large number of devices communicate with a base-station (BS) in a sporadic fashion. Device activity detection and channel estimation are central problems in such a scenario. Due to the large number of potential devices, the devices need to be assigned non-orthogonal signature sequences. The main objective of this paper is to show that by using random signature sequences and by exploiting sparsity in the user activity pattern, the joint user detection and channel estimation problem can be formulated as a compressed sensing single measurement vector (SMV) or multiple measurement vector (MMV) problem depending on whether the BS has a single antenna or multiple antennas and efficiently solved using an approximate message passing (AMP) algorithm. This paper proposes an AMP algorithm design that exploits the statistics of the wireless channel and provides an analytical characterization of the probabilities of false alarm and missed detection via state evolution. We consider two cases depending on whether or not the large-scale component of the channel fading is known at the BS and design the minimum mean squared error denoiser for AMP according to the channel statistics. Simulation results demonstrate the substantial advantage of exploiting the channel statistics in AMP design; however, knowing the large-scale fading component does not appear to offer tangible benefits. For the multiple-antenna case, we employ two different AMP algorithms, namely the AMP with vector denoiser and the parallel AMP-MMV, and quantify the benefit of deploying multiple antennas.

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"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "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 "a dimension reduction based joint activity detection and channel estimation algorithm for massive access"):
  with members:
    - "design of non orthogonal beamspace multiple access for cellular internet of things"
    - "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 signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "a new scaling law for activity detection in massive mimo systems"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "compressive sensing techniques for next generation wireless communications"

Connected through hyperedge HE-3 (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 signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"

Connected through hyperedge HE-4 (induced by source paper "jointly sparse signal recovery via deep auto encoder and parallel coordinate descent unrolling"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "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"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "deep networks for compressed image sensing"
    - "deep learning for massive mimo csi feedback"

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 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"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "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 "ml estimation and map estimation for device activities in grant free random access with interference"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "random access protocols for massive mimo"

Connected through hyperedge HE-7 (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-8 (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"

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)