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: "asymptotic performance of linear receivers in mimo fading channels"
  Abstract: Linear receivers are an attractive low-complexity alternative to optimal processing for multiple-antenna multiple-input multiple-output (MIMO) communications. In this paper, we characterize the information-theoretic performance of MIMO linear receivers in two different asymptotic regimes. For fixed number of antennas, we investigate the limit of error probability in the high-signal-to noise-ratio (SNR) regime in terms of the diversity-multiplexing tradeoff (DMT). Following this, we characterize the error probability for fixed SNR in the regime of large (but finite) number of antennas.As far as the DMT is concerned, we report a negative result: we show that both linear zero-forcing (ZF) and linear minimum mean- square error (MMSE) receivers achieve the same DMT, which is largely suboptimal even in the case where outer coding and deAcircnot coding is performed across the antennas. We also provide an apAcircnot proximate quantitative analysis of the markedly different behavior of the MMSE and ZF receivers at finite rate and nonasymptotic SNR, and show that while the ZF receiver achieves poor diversity at any finite rate, the MMSE receiver error curve slope flattens out progressively, as the coding rate increases. When SNR is fixed and the number of antennas becomes large, we show that the mutual information at the output of an MMSE or ZF linear receiver has fluctuations that converge in distribution to a Gaussian random variable, whose mean and variance can be characterized in closed form. This analysis extends to the linear reAcircnot ceiver case a well-known result previously obtained for the optimal receiver. Simulations reveal that the asymptotic analysis captures accurately the outage behavior of systems even with a moderate number of antennas.

Connected through hyperedge HE-1 (induced by source paper "comments on physical layer cryptography through massive mimo"):
  with members:
    - "physical layer cryptography through massive mimo"
    - "linear precoding of data and artificial noise in secure massive mimo systems"
    - "jamming aided secure communication in massive mimo rician channels"
    - "physical layer security for massive mimo an overview on passive eavesdropping and active attacks"
    - "on massive mimo physical layer cryptosystem"
    - "renyi divergence and kullback leibler divergence"
    - "the secrecy capacity of the mimo wiretap channel"

Connected through hyperedge HE-2 (induced by source paper "delay analysis of spatially coded mimo zfbf with retransmissions in random networks"):
  with members:
    - "capacity of cellular wireless network"
    - "impact of los nlos propagation and path loss in ultra dense cellular networks"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "managing interference correlation through random medium access"
    - "diversity polynomials for the analysis of temporal correlations in wireless networks"
    - "diversity loss due to interference correlation"
    - "open loop spatial multiplexing and diversity communications in ad hoc networks"
    - "throughput delay reliability tradeoff with arq in wireless ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning for joint mimo detection and channel decoding"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "deep learning based mimo communications"
    - "on deep learning based channel decoding"
    - "adam a method for stochastic optimization"
    - "soft input soft output single tree search sphere decoding"

Connected through hyperedge HE-4 (induced by source paper "closed form performance analysis of linear mimo receivers in general fading scenarios"):
  with members:
    - "performance of massive mimo uplink with zero forcing receivers under delayed channels"
    - "on the outage capacity of orthogonal space time block codes over multi cluster scattering mimo channels"
    - "schur complement based analysis of mimo zero forcing for rician fading"
    - "exact mimo zero forcing detection analysis for transmit correlated rician fading"

Connected through hyperedge HE-5 (induced by source paper "vlsi design of a nonparametric equalizer for massive mu mimo"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "optimally tuned nonparametric linear equalization for massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-6 (induced by source paper "optimally tuned nonparametric linear equalization for massive mu mimo systems"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"
    - "massive mimo how many antennas do we need"
    - "mimo transmission with residual transmit rf impairments"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-7 (induced by source paper "exact statistical characterization of 2 times2 gram matrices with arbitrary variance profile"):
  with members:
    - "performance analysis of mimo mrc in double correlated rayleigh environments"
    - "on the capacity of doubly correlated mimo channels"

Connected through hyperedge HE-8 (induced by source paper "asymptotic analysis of random lattices in high dimensions"):
  with members:
    - "fifty years of mimo detection the road to large scale mimos"
    - "effects of the lll reduction on the success probability of the babai point and on the complexity of sphere decoding"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "faster recursions in sphere decoding"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "on the limitations of the naive lattice decoding"

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)