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: "mimo detection for high order qam based on a gaussian tree approximation"
  Abstract: This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection.

Connected through hyperedge HE-1 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel"):
  with members:
    - "cube split a structured grassmannian constellation for non coherent simo communications"
    - "probabilistic mimo symbol detection with expectation consistency approximate inference"
    - "fifty years of mimo detection the road to large scale mimos"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a new class of upper bounds on the log partition function"
    - "on the capacity of large mimo block fading channels"

Connected through hyperedge HE-2 (induced by source paper "probabilistic mimo symbol detection with expectation consistency approximate inference"):
  with members:
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "capacity achieving iterative lmmse detection for mimo noma systems"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "spatially coupled ldpc codes constructed from protographs"
    - "optimality of large mimo detection via approximate message passing"
    - "fifty years of mimo detection the road to large scale mimos"
    - "spatially coupled sparse codes on graphs theory and practice"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"

Connected through hyperedge HE-3 (induced by source paper "large mimo detection schemes based on channel puncturing performance and complexity analysis"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "energy and spectral efficiency of very large multiuser mimo systems"

Connected through hyperedge HE-4 (induced by source paper "low complexity near optimal signal detection for uplink large scale mimo systems"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-5 (induced by source paper "near optimal linear precoding with low complexity for massive mimo"):
  with members:
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "linear precoding based on polynomial expansion large scale multi cell mimo systems"
    - "five disruptive technology directions for 5g"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "sum rates rate allocation and user scheduling for multi user mimo vector perturbation precoding"

Connected through hyperedge HE-6 (induced by source paper "channel hardening exploiting message passing chemp receiver in large scale mimo systems"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "sumis near optimal soft in soft out mimo detection with low and fixed complexity"
    - "low complexity detection in large dimension mimo isi channels using graphical models"
    - "high rate space time coded large mimo systems low complexity detection and channel estimation"

Connected through hyperedge HE-7 (induced by source paper "m ary detection and q ary decoding in large scale mimo a non binary belief propagation approach"):
  with members:
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "low complexity detection in large dimension mimo isi channels using graphical models"
    - "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels"

Connected through hyperedge HE-8 (induced by source paper "achieving a vanishing snr gap to exact lattice decoding at a subexponential complexity"):
  with members:
    - "on the proximity factors of lattice reduction aided decoding"
    - "efficient soft input soft output tree detection via an improved path metric"
    - "sphere decoding complexity exponent for decoding full rate codes over the quasi static mimo channel"
    - "decoding by sampling a randomized lattice algorithm for bounded distance decoding"
    - "faster recursions in sphere decoding"
    - "finite snr diversity multiplexing tradeoff via asymptotic analysis of large mimo systems"
    - "dmt optimality of lr aided linear decoders for a general class of channels lattice designs and system models"
    - "soft input soft output single tree search sphere 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)