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: "a general computation rule for lossy summaries messages with examples from equalization"
  Abstract: Elaborating on prior work by Minka, we formu- late a general computation rule for lossy messages. An impor- tant special case (with many applications in communications) is the conversion of "soft-bit" messages to Gaussian messages. By this method, the performance of a Kalman equalizer is improved, both for uncoded and coded transmission.

Connected through hyperedge HE-1 (induced by source paper "unifying message passing algorithms under the framework of constrained bethe free energy minimization"):
  with members:
    - "an iterative receiver for ofdm with sparsity based parametric channel estimation"
    - "hybrid vector perturbation precoding the blessing of approximate message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "gaussian message passing iterative detection for mimo noma systems with massive access"
    - "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
    - "optimality of large mimo detection via approximate message passing"
    - "s amp approximate message passing for general matrix ensembles"

Connected through hyperedge HE-2 (induced by source paper "probabilistic receiver architecture combining bp mf and ep for multi signal detection"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "a low complexity receiver using combined bp mf for joint channel estimation and decoding in ofdm systems"
    - "a low complexity ofdm receiver with combined gamp and mf message passing"
    - "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"
    - "approximate joint map detection of co channel signals"
    - "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"
    - "joint sparse channel estimation and decoding continuous and discrete domain sparsity"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-3 (induced by source paper "a bp mf ep based iterative receiver for joint phase noise estimation equalization and decoding"):
  with members:
    - "calculation of the performance of communication systems from measured oscillator phase noise"
    - "message passing algorithms for channel estimation and decoding using approximate inference"
    - "merging belief propagation and the mean field approximation a free energy approach"

Connected through hyperedge HE-4 (induced by source paper "turbo equalization using partial gaussian approximation"):
  with members:
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-5 (induced by source paper "message passing receiver for joint channel estimation and decoding in 3d massive mimo ofdm systems"):
  with members:
    - "pilot beam pattern design for channel estimation in massive mimo systems"
    - "bilinear generalized approximate message passing part i derivation"
    - "massive mimo with optimal power and training duration allocation"
    - "massive mimo for next generation wireless systems"
    - "kronecker product correlation model and limited feedback codebook design in a 3d channel model"
    - "variational free energies for compressed sensing"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-6 (induced by source paper "the hilbert space of probability mass functions and applications on probabilistic inference"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-7 (induced by source paper "multiple access demodulation in the lifted signal graph with spatial coupling"):
  with members:
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "connection between annealed free energy and belief propagation on random factor graph ensembles"
    - "tight bounds on the capacity of binary input random cdma systems"
    - "on maximum contention free interleavers and permutation polynomials over integer rings"

Connected through hyperedge HE-8 (induced by source paper "the trapping redundancy of linear block codes"):
  with members:
    - "permutation decoding and the stopping redundancy hierarchy of cyclic and extended cyclic codes"
    - "on parity check collections for iterative erasure decoding that correct all correctable erasure patterns of a given size"
    - "on the hardness of approximating stopping and trapping sets in ldpc codes"
    - "shortened array codes of large girth"
    - "improving convergence of belief propagation decoding"
    - "characterizations of pseudo codewords of ldpc codes"
    - "on the stopping distance and the stopping redundancy of codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

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)