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 content identification with lossy recovery"):
  Abstract: We revisit the high-dimensional content identification with lossy recovery problem (Tuncel and G\"und\"uz, 2014) and establish an exponential strong converse theorem. As a corollary of the exponential strong converse theorem, we derive an upper bound on the joint identification-error and excess-distortion exponent for the problem. Our main results can be specialized to the biometrical identification problem~(Willems, 2003) and the content identification problem~(Tuncel, 2009) since these two problems are both special cases of the content identification with lossy recovery problem. We leverage the information spectrum method introduced by Oohama and adapt the strong converse techniques therein to be applicable to the problem at hand.

Includes members:
  Member "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "second order region for gray wyner network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the dispersion of the gauss markov source")
  Member "the dispersion of universal joint source channel coding for arbitrary sources and additive channels":
    also connected through other hyperedges:
      (none)
  Member "non asymptotic converse bounds and refined asymptotics for two lossy source coding problems":
    also connected through other hyperedges:
      (none)
  Member "wyner s common information under r enyi divergence measures":
    also connected through other hyperedges:
      (none)
  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 "exponential strong converse for successive refinement with causal decoder side information")
  Member "new strong converse for asymmetric broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "achievable moderate deviations asymptotics for streaming compression of correlated sources":
    also connected through other hyperedges:
      (none)

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)