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: "analysis of ldgm and compound codes for lossy compression and binning"
  Abstract: Recent work has suggested that low-density gener- ator matrix (LDGM) codes are likely to be effective for lossy source coding problems. We derive rigorous upper bounds on the effective rate-distortion function of LDGM codes for the binary symmetric source, showing that they quickly approach the rate-distortion function as the degree increases. We also compare and contrast the standard LDGM construction with a compound LDPC/LDGM construction introduced in our previous work, which provably saturates the rate-distortion bound with finite degrees. Moreover, this compound construction can be used to generate nested codes that are simultaneously good as source and channel codes, and are hence well-suited to source/channel coding with side information. The sparse and high-girth graphical structure of our constructions render them well-suited to message-passing encoding. For channel coding problems, codes based on graphical con- structions, including turbo codes and low-density parity c heck (LDPC) codes, are widely used and well understood (16). However, many communication problems involve aspects of quantization, or quantization in conjunction with channel coding. Well-known examples include lossy data compression, source coding with side information (the Wyner-Ziv problem), and channel coding with side information (the Gelfand-Pinsker problem). For such communication problems involving quanti- zation, the use of sparse graphical codes and message-passing

Connected through hyperedge HE-1 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation"):
  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"
    - "average entropy functions"
    - "design and analysis of ldgm based codes for mse quantization"
    - "the case for structured random codes in network capacity theorems"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"

Connected through hyperedge HE-2 (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"
    - "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"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

Connected through hyperedge HE-3 (induced by source paper "design and analysis of ldgm based codes for mse quantization"):
  with members:
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "the generalized area theorem and some of its consequences"
    - "performance versus complexity per iteration for low density parity check codes an information theoretic approach"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
    - "iterative quantization using codes on graphs"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on the rate distortion function of ldgm codes"):
  with members:
    - "lower bounds on the rate distortion function of individual ldgm codes"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "low density codes achieve the rate distortion bound"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "iterative quantization using codes on graphs"
    - "approximating the satisfiability threshold for random k xor formulas"

Connected through hyperedge HE-5 (induced by source paper "lower bounds on the rate distortion function of individual ldgm codes"):
  with members:
    - "lower bounds on the rate distortion function of ldgm codes"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "low density codes achieve the rate distortion bound"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
    - "iterative quantization using codes on graphs"

Connected through hyperedge HE-6 (induced by source paper "low density graph codes that are optimal for source channel coding and binning"):
  with members:
    - "low density codes achieve the rate distortion bound"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
    - "iterative quantization using codes on graphs"
    - "approximating the satisfiability threshold for random k xor formulas"

Connected through hyperedge HE-7 (induced by source paper "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"):
  with members:
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"
    - "iterative quantization using codes on graphs"

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)