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: "binary quantization using belief propagation with decimation over factor graphs of ldgm codes"
  Abstract: We propose a new algorithm for binary quantization based on the Belief Propagation algorithm with decimation over factor graphs of Low Density Generator Matrix (LDGM) codes. This algorithm, which we call Bias Propagation (BiP), can be considered as a special case of the Survey Propagation algorithm proposed for binary quantization by Wainwright et al. [8]. It achieves the same near-optimal rate-distortion performance with a substantially simpler framework and 10-100 times faster implementation. We thus challenge the widespread belief that binary quantization based on sparse linear codes cannot be solved by simple Belief Propagation algorithms. Finally, we give examples of suitably irregular LDGM codes that work with the BiP algorithm and show their performance.

Connected through hyperedge HE-1 (induced by source paper "successive wyner ziv coding for the binary ceo problem under logarithmic loss"):
  with members:
    - "successive wyner ziv coding for the binary ceo problem under log loss"
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "remote source coding under gaussian noise dueling roles of power and entropy power"
    - "the ceo problem with rth power of difference and logarithmic distortions"
    - "on the optimal fronthaul compression and decoding strategies for uplink cloud radio access networks"
    - "the non regular ceo problem"
    - "how to achieve the capacity of asymmetric channels"
    - "multiterminal source coding under logarithmic loss"

Connected through hyperedge HE-2 (induced by source paper "successive wyner ziv coding for the binary ceo problem under log loss"):
  with members:
    - "analysis and code design for the binary ceo problem under logarithmic loss"
    - "multiterminal source coding under logarithmic loss"
    - "distributed source coding of correlated gaussian remote sources"
    - "successive wyner ziv coding scheme and its application to the quadratic gaussian ceo problem"

Connected through hyperedge HE-3 (induced by source paper "a simple proof of maxwell saturation for coupled scalar recursions"):
  with members:
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "approaching the rate distortion limit with spatial coupling belief propagation and decimation"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "nonbinary spatially coupled ldpc codes on the binary erasure channel"
    - "a proof of threshold saturation for spatially coupled ldpc codes on bms channels"
    - "thresholds of spatially coupled systems via lyapunov s method"
    - "spatial coupling as a proof technique"
    - "a simple proof of threshold saturation for coupled scalar recursions"

Connected through hyperedge HE-4 (induced by source paper "approaching the rate distortion limit with spatial coupling belief propagation and decimation"):
  with members:
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "spatial coupling as a proof technique"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "lossy source coding via spatially coupled ldgm ensembles"
    - "chains of mean field models"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-5 (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"
    - "low density graph codes that are optimal for source channel coding and binning"
    - "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-6 (induced by source paper "blahut arimoto algorithm and code design for action dependent source coding problems"):
  with members:
    - "polar codes are optimal for lossy source coding"
    - "source coding with a side information vending machine"
    - "iterative quantization using codes on graphs"

Connected through hyperedge HE-7 (induced by source paper "lossy source coding via spatially coupled ldgm ensembles"):
  with members:
    - "threshold saturation in spatially coupled constraint satisfaction problems"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "chains of mean field models"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "coupled graphical models and their thresholds"
    - "universal rateless codes from coupled lt codes"
    - "asymptotically regular ldpc codes with linear distance growth and thresholds close to capacity"
    - "the effect of spatial coupling on compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "graph based code design for quadratic gaussian wyner ziv problem with arbitrary side information"):
  with members:
    - "polar codes are optimal for lossy source coding"
    - "efficient ldpc codes over gf q for lossy data compression"

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)