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 "arithmetic coding based multi composition codes for bit level distribution matching"):
  Abstract: A distribution matcher (DM) encodes a binary input data sequence into a sequence of symbols (codeword) with desired target probability distribution. The set of the output codewords constitutes a codebook (or code) of a DM. Constant-composition DM (CCDM) uses arithmetic coding to efficiently encode data into codewords from a constant-composition (CC) codebook. The CC constraint limits the size of the codebook, and hence the coding rate of the CCDM. The performance of CCDM degrades with decreasing output length. To improve the performance for short transmission blocks we present a class of multi-composition (MC) codes and an efficient arithmetic coding scheme for encoding and decoding. The resulting multi-composition DM (MCDM) is able to encode more data into distribution matched codewords than the CCDM and achieves lower KL divergence, especially for short block messages.

Includes members:
  Member "shell mapping for distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finite precision implementation of arithmetic coding based distribution matchers")
  Member "divergence scaling of fixed length binary output one to one distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shaped on off keying using polar codes")
  Member "constant composition distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rate constrained shaping codes for structured sources")
  Member "block to block distribution matching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constant composition distribution matching")

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)