human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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

Includes members:
  Member "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low density majority codes and the problem of graceful degradation")
  Member "iterative quantization using codes on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on privacy amplification lossy compression and their duality to channel coding")
  Member "approximating the satisfiability threshold for random k xor formulas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lower bounds on the rate distortion function of ldgm codes")

Question: Please tell me which class the source paper 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)