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: "robust estimates of covariance matrices in the large dimensional regime"
  Abstract: This paper studies the limiting behavior of a class of robust population covariance matrix estimators, originally due to Maronna in 1976, in the regime where both the number of available samples and the population size grow large. Using tools from random matrix theory, we prove that, for sample vectors made of independent entries having some moment conditions, the difference between the sample covariance matrix and (a scaled version of) such robust estimator tends to zero in spectral norm, almost surely. This result can be applied to various statistical methods arising from random matrix theory that can be made robust without altering their first order behavior.

Connected through hyperedge HE-1 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "on the theorem of uniform recovery of random sampling matrices"

Connected through hyperedge HE-2 (induced by source paper "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

Connected through hyperedge HE-3 (induced by source paper "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"):
  with members:
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "blind pilot decontamination"
    - "massive mimo and small cells improving energy efficiency by optimal soft cell coordination"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"
    - "hardware impairments in large scale miso systems energy efficiency estimation and capacity limits"
    - "linear precoding based on polynomial expansion reducing complexity in massive mimo"
    - "receive combining vs multi stream multiplexing in downlink systems with multi antenna users"

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)