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: "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
  Abstract: We describe message-passing and decimation approaches for lossy source coding using low-density generator matrix (LDGM) codes. In particular, this paper addresses the problem of encoding a Bernoulli(0.5) source: for randomly generated LDGM codes with suitably irregular degree distributions, our methods yield performance very close to the rate distortion limit over a range of rates. Our approach is inspired by the survey propagation (SP) algorithm, originally developed by Mezard et al. for solving random satisfiability problems. Previous work by Maneva et al. shows how SP can be understood as belief propagation (BP) for an alternative representation of satisfiability problems. In analogy to this connection, our approach is to define a family of Markov random fields over generalized codewords, from which local message-passing rules can be derived in the standard way. The overall source encoding method is based on message-passing, setting a subset of bits to their preferred values (decimation), and reducing the code.

Connected through hyperedge HE-1 (induced by source paper "low density majority codes and the problem of graceful degradation"):
  with members:
    - "a lower bound on the expected distortion of joint source channel coding"
    - "low complexity concatenated ldpc staircase codes"
    - "comparison of channels criteria for domination by a symmetric channel"
    - "deep joint source channel coding for wireless image transmission"
    - "strong data processing inequalities for channels and bayesian networks"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "protograph based raptor like ldpc codes"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-2 (induced by source paper "on low density majority codes"):
  with members:
    - "a lower bound on the expected distortion of joint source channel coding"
    - "low complexity concatenated ldpc staircase codes"
    - "deep joint source channel coding for wireless image transmission"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "protograph based raptor like ldpc codes"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-3 (induced by source paper "approaching maximum embedding efficiency on small covers using staircase generator codes"):
  with members:
    - "capacity of steganographic channels"

Connected through hyperedge HE-4 (induced by source paper "lossy source coding via spatially coupled ldgm ensembles"):
  with members:
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "chains of mean field models"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "coupled graphical models and their thresholds"
    - "universal rateless codes from coupled lt codes"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
    - "the effect of spatial coupling on compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "lossy compression of discrete sources via the viterbi algorithm"):
  with members:
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "rate distortion via markov chain monte carlo"

Connected through hyperedge HE-6 (induced by source paper "polar codes are optimal for lossy source coding"):
  with members:
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "performance of polar codes for channel and source coding"
    - "polar codes characterization of exponent bounds and constructions"
    - "polarization for arbitrary discrete memoryless channels"
    - "on the rate of channel polarization"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "solving constraint satisfaction problems through belief propagation guided decimation"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"

Connected through hyperedge HE-7 (induced by source paper "efficient ldpc codes over gf q for lossy data compression"):
  with members:
    - "lower bounds on the rate distortion function of ldgm codes"
    - "lower bounds on the rate distortion function of individual ldgm codes"
    - "encoding for the blackwell channel with reinforced belief propagation"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "analysis of ldgm and compound codes for lossy compression and binning"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "design and analysis of nonbinary ldpc codes for arbitrary discrete memoryless channels"
    - "statistical mechanical approach to lossy data compression theory and practice"

Connected through hyperedge HE-8 (induced by source paper "lossy compression in near linear time via efficient random codebooks and databases"):
  with members:
    - "an implementable scheme for universal lossy compression of discrete markov sources"
    - "rate distortion via markov chain monte carlo"
    - "low density codes achieve the rate distortion bound"

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)