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 "a new achievable rate distortion region for distributed source coding"):
  Abstract: In this work, lossy distributed compression of pairs of correlated sources is considered. Conventionally, Shannon's random coding arguments --- using randomly generated unstructured codebooks whose blocklength is taken to be asymptotically large --- are used to derive achievability results. However, it was recently observed that in various multi-terminal communications scenarios, using random codes with constant finite blocklength may lead to improved achievable regions compared to the conventional approach. In other words, in some network communication scenarios, there is a finite optimal value in the blocklength of the randomly generated code used for distributed processing of information sources. Motivated by this, a coding scheme is proposed which consists of two codebook layers: i) the primary codebook which has constant finite blocklength, and ii) the secondary codebook whose blocklength is taken to be asymptotically large. The achievable region is analyzed in two steps. In the first step, a characterization of the achievable region is derived using information measures which are functions of multi-letter probability distributions. In the next step, a computable single-letter inner-bound to the achievable region is derived. It is shown through several examples that the resulting rate-distortion region is strictly larger than the Berger Tung achievable region.

Includes members:
  Member "communicating correlated sources over an interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communicating correlated sources over mac and interference channels i separation based schemes")
  Member "on the sub optimality of single letter coding in multi terminal communications":
    also connected through other hyperedges:
      (none)
  Member "on the sub optimality of single letter coding over networks":
    also connected through other hyperedges:
      (none)
  Member "communicating correlated sources over a mac in the absence of a g acs k orner common part":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "communicating correlated sources over an interference channel")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finite sample analysis of image registration")

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)