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: "lower bounds on the rate distortion function of ldgm codes"
  Abstract: A recent line of work has focused on the use of low-density generator matrix (LDGM) codes for lossy source coding. In this paper, wedevelop a generic technique for deriving lower bounds on the rate-distortion functions of binary linear codes, with particular interest on the effect of bounded degrees. The underlying ideas can be viewing as the source coding analog of the classical result of Gallager, providing bounds for channel coding over the binary symmetric channel using bounded degree LDPC codes. We illustrate this method for different random ensembles of LDGM codes, including the check-regular ensemble and bit-check-regular ensembles, by deriving explicit lower bounds on their rate-distortion performance as a function of the degrees.

Connected through hyperedge HE-1 (induced by source paper "on locally decodable source coding"):
  with members:
    - "zero delay and causal single user and multi user lossy source coding with decoder side information"
    - "fixed length lossy compression in the finite blocklength regime"
    - "locally repairable codes"
    - "malleable coding compressed palimpsests"
    - "lower bounds on the rate distortion function of individual ldgm codes"

Connected through hyperedge HE-2 (induced by source paper "optimal lempel ziv based lossy compression for memoryless data how to make the right mistakes"):
  with members:
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "low density codes achieve the rate distortion bound"

Connected through hyperedge HE-3 (induced by source paper "efficient ldpc codes over gf q for lossy data compression"):
  with members:
    - "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"
    - "lossy source encoding via message passing and decimation over generalized codewords of ldgm codes"

Connected through hyperedge HE-4 (induced by source paper "lower bounds on the rate distortion function of individual ldgm codes"):
  with members:
    - "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 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-5 (induced by source paper "hash property and coding theorems for sparse matrices and maximum likelihood coding"):
  with members:
    - "construction of codes for wiretap channel and secret key agreement from correlated source outputs by using sparse matrices"
    - "hash property and fixed rate universal coding theorems"
    - "lower bounds on the rate distortion function of individual ldgm codes"
    - "low density codes achieve the rate distortion bound"
    - "low density constructions can achieve the wyner ziv and gelfand pinsker bounds"

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)