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 "optimal equivocation in secrecy systems a special case of distortion based characterization"):
  Abstract: Recent work characterizing the optimal performance of secrecy systems has made use of a distortion-like metric for partial secrecy as a replacement for the more traditional metric of equivocation. In this work we use the log-loss function to show that the optimal performance limits characterized by equivocation are, in fact, special cases of distortion-based counterparts. This observation illuminates why equivocation doesn't tell the whole story of secrecy. It also justifies the causal-disclosure framework for secrecy (past source symbols and actions revealed to the eavesdropper).

Includes members:
  Member "secrecy is cheap if the adversary must reconstruct":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a large deviations approach to secure lossy compression")
  Member "using a secret key to foil an eavesdropper":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large deviations approach to secure lossy compression")
  Member "multiterminal source coding under logarithmic loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "multiterminal source coding with an entropy based distortion measure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vector gaussian ceo problem under logarithmic loss")
  Member "secure lossy source coding with side information at the decoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wiretap channels with random states non causally available at the encoder")
  Member "a framework for partial secrecy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure cascade channel synthesis")
  Member "secure source coding with a helper":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies")
  Member "secure lossless compression with side information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "privacy aware distributed hypothesis testing")

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)