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: "optimality of large mimo detection via approximate message passing"
  Abstract: Optimal data detection in multiple-input multiple-output (MIMO) communication systems with a large number of antennas at both ends of the wireless link entails prohibitive computational complexity. In order to reduce the computational complexity, a variety of sub-optimal detection algorithms have been proposed in the literature. In this paper, we analyze the optimality of a novel data-detection method for large MIMO systems that relies on approximate message passing (AMP). We show that our algorithm, referred to as individually-optimal (IO) large-MIMO AMP (short IO-LAMA), is able to perform IO data detection given certain conditions on the MIMO system and the constellation set (e.g., QAM or PSK) are met.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "deep hypernetwork based mimo detection"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "the roadmap to 6g ai empowered wireless networks"
    - "a model driven deep learning network for mimo detection"
    - "learning feed forward one shot learners"
    - "adam a method for stochastic optimization"
    - "mixed integer quadratic programming is in np"

Connected through hyperedge HE-3 (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 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"

Connected through hyperedge HE-4 (induced by source paper "learning to detect"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "an introduction to deep learning for the physical layer"
    - "learning approximate neural estimators for wireless channel state information"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "deep learning based mimo communications"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"

Connected through hyperedge HE-5 (induced by source paper "learning to search for mimo detection"):
  with members:
    - "learning to detect"
    - "multilevel mimo detection with deep learning"
    - "two stage lasso admm signal detection algorithm for large scale mimo"
    - "a model driven deep learning network for mimo detection"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to optimize"
    - "fifty years of mimo detection the road to large scale mimos"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a simple bound on the ber of the map decoder for massive mimo systems"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "massive mimo for next generation wireless systems"
    - "a framework to characterize performance of lasso algorithms"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-7 (induced by source paper "alpha belief propagation as fully factorized approximation"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "inference in probabilistic graphical models by graph neural networks"
    - "deeply learning the messages in message passing inference"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-8 (induced by source paper "a 354mb s 0 37mm 2 151mw 32 user 256 qam near map soft input soft output massive mu mimo data detector in 28nm cmos"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "channel hardening exploiting message passing chemp receiver in large scale 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)