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 "exponential strong converse for successive refinement with causal decoder side information"):
  Abstract: We consider the $k$-user successive refinement problem with causal decoder side information and derive an exponential strong converse theorem. The rate-distortion region for the problem can be derived as a straightforward extension of the two-user case by Maor and Merhav (2008). We show that for any rate-distortion tuple outside the rate-distortion region of the $k$-user successive refinement problem with causal decoder side information, the joint excess-distortion probability approaches one exponentially fast. Our proof follows by judiciously adapting the recently proposed strong converse technique by Oohama using the information spectrum method, the variational form of the rate-distortion region and H\"older's inequality. The lossy source coding problem with causal decoder side information considered by El Gamal and Weissman is a special case ($k=1$) of the current problem. Therefore, the exponential strong converse theorem for the El Gamal and Weissman problem follows as a corollary of our result.

Includes members:
  Member "strong converse using change of measure arguments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication for generating correlation a unifying survey")
  Member "exponential strong converse for content identification with lossy recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "resolution limits of noisy 20 questions estimation")
  Member "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strong converse using change of measure arguments")
  Member "new strong converse for asymmetric broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a new inequality related to proofs of strong converse theorems for source or channel networks")
  Member "a proof of the strong converse theorem for gaussian multiple access channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "exponent function for one helper source coding problem at rates outside the rate region":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a new inequality related to proofs of strong converse theorems for source or channel networks")
  Member "strong successive refinability and rate distortion complexity tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "second order region for gray wyner network")
  Member "asymptotic estimates in information theory with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)