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 individual ldgm codes"
  Abstract: We consider lossy compression of a binary symmetric source by means of a low-density generator-matrix code. We derive two lower bounds on the rate distortion function which are valid for any low-density generator-matrix code with a given node degree distribution L(x) on the set of generators and for any encoding algorithm. These bounds show that, due to the sparseness of the code, the performance is strictly bounded away from the Shannon rate-distortion function. In this sense, our bounds represent a natural generalization of Gallager's bound on the maximum rate at which low-density parity-check codes can be used for reliable transmission. Our bounds are similar in spirit to the technique recently developed by Dimakis, Wainwright, and Ramchandran, but they apply to individual codes.

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 ldgm codes"

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"
    - "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-3 (induced by source paper "lower bounds on the rate distortion function of 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"
    - "iterative quantization using codes on graphs"
    - "approximating the satisfiability threshold for random k xor formulas"

Connected through hyperedge HE-4 (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 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)