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 "optimal lempel ziv based lossy compression for memoryless data how to make the right mistakes"):
  Abstract: Compression refers to encoding data using bits, so that the representation uses as few bits as possible. Compression could be lossless: i.e. encoded data can be recovered exactly from its representation) or lossy where the data is compressed more than the lossless case, but can still be recovered to within prespecified distortion metric. In this paper, we prove the optimality of Codelet Parsing, a quasi-linear time algorithm for lossy compression of sequences of bits that are independently and identically distributed (\iid) and Hamming distortion. Codelet Parsing extends the lossless Lempel Ziv algorithm to the lossy case---a task that has been a focus of the source coding literature for better part of two decades now. Given \iid sequences $\x$, the expected length of the shortest lossy representation such that $\x$ can be reconstructed to within distortion $\dist$ is given by the rate distortion function, $\rd$. We prove the optimality of the Codelet Parsing algorithm for lossy compression of memoryless bit sequences. It splits the input sequence naturally into phrases, representing each phrase by a codelet, a potentially distorted phrase of the same length. The codelets in the lossy representation of a length-$n$ string ${\x}$ have length roughly $(\log n)/\rd$, and like the lossless Lempel Ziv algorithm, Codelet Parsing constructs codebooks logarithmic in the sequence length.

Includes members:
  Member "lossy compression of discrete sources via the viterbi algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "theoretical links between universal and bayesian compressed sensing algorithms")
  Member "lower bounds on the rate distortion function of ldgm codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on locally decodable source coding")
  Member "low density codes achieve the rate distortion bound":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization")

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)