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 "low density graph codes that are optimal for source channel coding and binning"):
  Abstract: We describe and analyze the joint source/channel coding properties of a class of sparse graphical codes based on compounding a low-density generator matrix (LDGM) code with a low-density parity check (LDPC) code. Our first pair of theorems establish that there exist codes from this ensemble, with all degrees remaining bounded independently of block length, that are simultaneously optimal as both source and channel codes when encoding and decoding are performed optimally. More precisely, in the context of lossy compression, we prove that finite degree constructions can achieve any pair $(R, D)$ on the rate-distortion curve of the binary symmetric source. In the context of channel coding, we prove that finite degree codes can achieve any pair $(C, p)$ on the capacity-noise curve of the binary symmetric channel. Next, we show that our compound construction has a nested structure that can be exploited to achieve the Wyner-Ziv bound for source coding with side information (SCSI), as well as the Gelfand-Pinsker bound for channel coding with side information (CCSI). Although the current results are based on optimal encoding and decoding, the proposed graphical codes have sparse structure and high girth that renders them well-suited to message-passing and other efficient decoding procedures.

Includes members:
  Member "analysis of ldgm and compound codes for lossy compression and binning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation")
  Member "low density codes achieve the rate distortion bound":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization")
  Member "low density constructions can achieve the wyner ziv and gelfand pinsker bounds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analytical framework of ldgm based iterative quantization with decimation")
  Member "capacity achieving ensembles for the binary erasure channel with bounded complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "log logarithmic time pruned polar coding")
  Member "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (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)