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 "strong converse using change of measure arguments"):
  Abstract: The strong converse for a coding theorem shows that the optimal asymptotic rate possible with vanishing error cannot be improved by allowing a fixed error. Building on a method introduced by Gu and Effros for centralized coding problems, we develop a general and simple recipe for proving strong converse that is applicable for distributed problems as well. Heuristically, our proof of strong converse mimics the standard steps for proving a weak converse, except that we apply those steps to a modified distribution obtained by conditioning the original distribution on the event that no error occurs. A key component of our recipe is the replacement of the hard Markov constraints implied by the distributed nature of the problem with a soft information cost using a variational formula introduced by Oohama. We illustrate our method by providing a short proof of the strong converse for the Wyner-Ziv problem and new strong converse theorems for interactive function computation, common randomness and secret key agreement, and the wiretap channel.

Includes members:
  Member "a new proof of nonsignalling multiprover parallel repetition theorem":
    also connected through other hyperedges:
      (none)
  Member "communication for generating correlation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coordination through shared randomness")
  Member "communication for generating correlation a unifying survey":
    also connected through other hyperedges:
      (none)
  Member "improved finite blocklength converses for slepian wolf coding via linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimax theorems for finite blocklength lossy joint source channel coding over an avc")
  Member "second order region for gray wyner network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the dispersion of the gauss markov source")
  Member "strong converses are just edge removal properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "negligible cooperation contrasting the maximal and average error cases")
  Member "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "wiretap channel capacity secrecy criteria strong converse and phase change":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inducing information stability and applications thereof to obtaining information theoretic necessary conditions directly from operational requirements")

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)