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: "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.

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "decoding complexity of irregular ldgm ldpc codes over the bisom channels"):
  with members:
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "applications of ldpc codes to the wiretap channel"

Connected through hyperedge HE-3 (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"
    - "solving constraint satisfaction problems through belief propagation guided decimation"
    - "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

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)