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 achievable rates of decentralized equalization in massive mu mimo systems"
  Abstract: Massive multi-user (MU) multiple-input multiple-output (MIMO) promises significant gains in spectral efficiency compared to traditional, small-scale MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or minimum mean-square error (MMSE)-based methods, typically rely on centralized processing at the base station (BS), which results in (i) excessively high interconnect and chip input/output data rates, and (ii) high computational complexity. In this paper, we investigate the achievable rates of decentralized equalization that mitigates both of these issues. We consider two distinct BS architectures that partition the antenna array into clusters, each associated with independent radio-frequency chains and signal processing hardware, and the results of each cluster are fused in a feedforward network. For both architectures, we consider ZF, MMSE, and a novel, non-linear equalization algorithm that builds upon approximate message passing (AMP), and we theoretically analyze the achievable rates of these methods. Our results demonstrate that decentralized equalization with our AMP-based methods incurs no or only a negligible loss in terms of achievable rates compared to that of centralized solutions.

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"
    - "optimally tuned nonparametric linear equalization for 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 "design trade offs for decentralized baseband processing in massive mu mimo systems"):
  with members:
    - "decentralized coordinate descent data detection and precoding for massive mu mimo"
    - "decentralized equalization with feedforward architectures for massive mu mimo"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "decentralized baseband processing for massive mu mimo systems"
    - "quantized precoding for massive mu mimo"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "decentralized coordinate descent data detection and precoding for massive mu mimo"):
  with members:
    - "decentralized equalization with feedforward architectures for massive mu mimo"
    - "feedforward architectures for decentralized precoding in massive mu mimo systems"
    - "decentralized baseband processing for massive mu mimo systems"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-4 (induced by source paper "feedforward architectures for decentralized precoding in massive mu mimo systems"):
  with members:
    - "decentralized baseband processing for massive mu mimo systems"
    - "cell free massive mimo versus small cells"
    - "quantized precoding for massive mu mimo"
    - "what will 5g be"

Connected through hyperedge HE-5 (induced by source paper "decentralized baseband processing for massive mu mimo systems"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "system architecture and key technologies for 5g heterogeneous cloud radio access networks"
    - "linear large scale mimo data detection for 5g multi carrier waveform candidates"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "distributed robust multicell coordinated beamforming with imperfect csi an admm approach"

Connected through hyperedge HE-6 (induced by source paper "approximate gram matrix interpolation for wideband massive mu mimo"):
  with members:
    - "nr wide bandwidth operations"
    - "on the performance of mismatched data detection in large mimo systems"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "conjugate gradient based soft output detection and precoding in massive mimo systems"
    - "massive mimo for next generation wireless systems"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "per antenna constant envelope precoding for large multi user mimo systems"

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)