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: "optimally tuned nonparametric linear equalization for massive mu mimo systems"
  Abstract: This paper deals with linear equalization in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. We first provide simple conditions on the antenna configuration for which the well-known linear minimum mean-square error (L-MMSE) equalizer provides near-optimal spectral efficiency, and we analyze its performance in the presence of parameter mismatches in the signal and/or noise powers. We then propose a novel, optimally-tuned NOnParametric Equalizer (NOPE) for massive MU-MIMO systems, which avoids knowledge of the transmit signal and noise powers altogether. We show that NOPE achieves the same performance as that of the L-MMSE equalizer in the large-antenna limit, and we demonstrate its efficacy in realistic, finite-dimensional systems. From a practical perspective, NOPE is computationally efficient and avoids dedicated training that is typically required for parameter estimation

Connected through hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo"):
  with members:
    - "gaussian message passing for overloaded massive mimo noma"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "efficient dsp and circuit architectures for massive mimo state of the art and future directions"
    - "decentralized baseband processing for massive mu mimo systems"
    - "on the achievable rates of decentralized equalization in massive mu mimo systems"
    - "on the performance of mismatched data detection in large mimo systems"
    - "performance trade offs in multi processor approximate message passing"
    - "the world s first real time testbed for massive mimo design implementation and validation"

Connected through hyperedge HE-2 (induced by source paper "vlsi design of a nonparametric equalizer for massive mu mimo"):
  with members:
    - "1 bit massive mu mimo precoding in vlsi"
    - "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"
    - "massive mimo how many antennas do we need"

Connected through hyperedge HE-3 (induced by source paper "vlsi designs for joint channel estimation and data detection in large simo wireless systems"):
  with members:
    - "efficient optimal joint channel estimation and data detection for massive mimo systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "joint channel estimation data detection in mimo fbmc oqam systems a tensor based approach"
    - "data detection in large multi antenna wireless systems via approximate semidefinite relaxation"
    - "high throughput data detection for massive mu mimo ofdm using coordinate descent"
    - "optimal non coherent data detection for massive simo wireless systems with general constellations a polynomial complexity solution"
    - "fifty years of mimo detection the road to large scale mimos"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"

Connected through hyperedge HE-4 (induced by source paper "low complexity and statistically robust beamformer design for massive mimo systems"):
  with members:
    - "on the ergodic rate lower bounds with applications to massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "optimality of large mimo detection via approximate message passing"
    - "fifty years of mimo detection the road to large scale mimos"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

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)