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 factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"
  Abstract: We propose a novel receiver for orthogonal frequency division multiplexing (OFDM) transmissions in impulsive noise environments. Impulsive noise arises in many modern wireless and wireline communication systems, such as Wi-Fi and powerline communications, due to uncoordinated interference that is much stronger than thermal noise. We first show that the bit-error-rate optimal receiver jointly estimates the propagation channel coefficients, the noise impulses, the finite-alphabet symbols, and the unknown bits. We then propose a near-optimal yet computationally tractable approach to this joint estimation problem using loopy belief propagation. In particular, we merge the recently proposed “generalized approximate message passing” (GAMP) algorithm with the forward-backward algorithm and soft-input soft-output decoding using a “turbo” approach. Numerical results indicate that the proposed receiver drastically outperforms existing receivers under impulsive noise and comes within 1 dB of the matched-filter bound. Meanwhile, with N tones, the proposed factor-graph-based receiver has only O(NlogN) complexity, and it can be parallelized.

Connected through hyperedge HE-1 (induced by source paper "capacity optimality of amp in coded systems"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "a unified framework of state evolution for message passing algorithms"
    - "optimal data detection in large mimo"
    - "near gaussian multiple access channel capacity detection and decoding"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-3 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"

Connected through hyperedge HE-5 (induced by source paper "hyperspectral unmixing via turbo bilinear approximate message passing"):
  with members:
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-6 (induced by source paper "blind sensor calibration using approximate message passing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "on convergence of approximate message passing"
    - "blind calibration in compressed sensing using message passing algorithms"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Connected through hyperedge HE-7 (induced by source paper "optimality of large mimo detection via approximate message passing"):
  with members:
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "channel hardening exploiting message passing chemp receiver in large scale mimo systems"
    - "sumis near optimal soft in soft out mimo detection with low and fixed complexity"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-8 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

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)