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: "on the capacity of large mimo block fading channels"
  Abstract: We characterize the capacity of Rayleigh block-fading multiple-input multiple-output (MIMO) channels in the noncoherent setting where transmitter and receiver have no a priori knowledge of the realizations of the fading channel. We prove that unitary space-time modulation (USTM) is not capacity-achieving in the high signal-to-noise ratio (SNR) regime when the total number of antennas exceeds the coherence time of the fading channel (expressed in multiples of the symbol duration), a situation that is relevant for MIMO systems with large antenna arrays (large-MIMO systems). This result settles a conjecture by Zheng & Tse (2002) in the affirmative. The capacity-achieving input signal, which we refer to as Beta-variate space-time modulation (BSTM), turns out to be the product of a unitary isotropically distributed random matrix, and a diagonal matrix whose nonzero entries are distributed as the square-root of the eigenvalues of a Beta-distributed random matrix of appropriate size. Numerical results illustrate that using BSTM instead of USTM in large-MIMO systems yields a rate gain as large as 13% for SNR values of practical interest.

Connected through hyperedge HE-1 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel"):
  with members:
    - "cube split a structured grassmannian constellation for non coherent simo communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "fifty years of mimo detection the road to large scale mimos"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a new class of upper bounds on the log partition function"
    - "mimo detection for high order qam based on a gaussian tree approximation"

Connected through hyperedge HE-2 (induced by source paper "saddlepoint approximations for rayleigh block fading channels"):
  with members:
    - "low complexity joint channel estimation and list decoding of short codes"
    - "short packets over block memoryless fading channels pilot assisted or noncoherent transmission"
    - "on single antenna rayleigh block fading channels at finite blocklength"
    - "64 apsk constellation and mapping optimization for satellite broadcasting using genetic algorithms"
    - "short packet communications over multiple antenna rayleigh fading channels"

Connected through hyperedge HE-3 (induced by source paper "cube split a structured grassmannian constellation for non coherent simo communications"):
  with members:
    - "fast approximate construction of best complex antipodal spherical codes"
    - "cube split structured quantizers on the grassmannian of lines"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

Connected through hyperedge HE-4 (induced by source paper "on the spectral efficiency of noncooperative uplink massive mimo systems"):
  with members:
    - "massive mimo ten myths and one critical question"
    - "optimal user cell association for massive mimo wireless networks"
    - "massive mimo for maximal spectral efficiency how many users and pilots should be allocated"
    - "large system analysis of correlated mimo multiple access channels with arbitrary signaling in the presence of interference"
    - "mutual information of iid complex gaussian signals on block rayleigh faded channels"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-5 (induced by source paper "fast analog transmission for high mobility wireless data acquisition in edge learning"):
  with members:
    - "automatic recognition of space time constellations by learning on the grassmann manifold"
    - "communication efficient learning of deep networks from decentralized data"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "federated optimization distributed optimization beyond the datacenter"
    - "five disruptive technology directions for 5g"
    - "robust analog function computation via wireless multiple access channels"

Connected through hyperedge HE-6 (induced by source paper "automatic recognition of space time constellations by learning on the grassmann manifold"):
  with members:
    - "inference from randomized transmissions by many backscatter sensors"
    - "deep architectures for modulation recognition"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

Connected through hyperedge HE-7 (induced by source paper "the optimal dof region for the two user non coherent simo multiple access channel"):
  with members:
    - "coherence disparity in broadcast and multiple access channels"

Connected through hyperedge HE-8 (induced by source paper "throughput analysis of massive mimo uplink with low resolution adcs"):
  with members:
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "channel estimation and uplink achievable rates in one bit massive mimo systems"
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "massive mimo ten myths and one critical question"
    - "one bit massive mimo channel estimation and high order modulations"

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)