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 unified approach to source and message compression"):
  Abstract: We study the problem of source and message compression in the one-shot setting for the point-to-point and multi-party scenario (with and without side information). We derive achievability results for these tasks in a unified manner, using the techniques of convex-split, which was introduced in [Anshu,Devabathini and Jain 2014] and position based decoding introduced in [Anshu, Jain and Warsi 2017], which in turn uses hypothesis testing between distributions. These results are in terms of smooth max divergence and smooth hypothesis testing divergence. As a by-product of the tasks studied in this work, we obtain several known source compression results (originally studied in the asymptotic and i.i.d. setting) in the one-shot case. #R##N#One of our achievability results includes the problem of message compression with side information, originally studied in [Braverman and Rao 2011]. We show that both our result and the result in [Braverman and Rao 2011] are near optimal in the one-shot setting by proving a converse bound.

Includes members:
  Member "a one shot achievability result for quantum state redistribution":
    also connected through other hyperedges:
      (none)
  Member "wiretap channels with random states non causally available at the encoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyless covert communication via channel state information")
  Member "simple one shot bounds for various source coding problems using smooth renyi quantities":
    also connected through other hyperedges:
      (none)
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "a technique for deriving one shot achievability results in network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "distributed channel synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite sample analysis of image registration")
  Member "information equals amortized communication":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication for generating correlation a unifying survey")

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)