human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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).

Connected through hyperedge HE-1 (induced by source paper "gaussian secure source coding and wyner s common information"):
  with members:
    - "an extremal inequality for long markov chains"
    - "secure coordination with a two sided helper"
    - "the lossy common information of correlated sources"
    - "wyner s common information generalizations and a new lossy source coding interpretation"
    - "a bit of secrecy for gaussian source compression"
    - "on secure source coding with side information at the encoder"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"

Connected through hyperedge HE-2 (induced by source paper "secure coordination with a two sided helper"):
  with members:
    - "the likelihood encoder for source coding"
    - "on secure source coding with side information at the encoder"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "using a secret key to foil an eavesdropper"
    - "multiterminal source coding under logarithmic loss"
    - "secure lossy source coding with side information at the decoders"
    - "a framework for partial secrecy"

Connected through hyperedge HE-3 (induced by source paper "secrecy in cascade networks"):
  with members:
    - "secure cascade channel synthesis"
    - "the likelihood encoder for source coding"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"
    - "secrecy is cheap if the adversary must reconstruct"
    - "multiterminal source coding under logarithmic loss"
    - "multiterminal source coding with an entropy based distortion measure"

Connected through hyperedge HE-4 (induced by source paper "rate distortion theory for secrecy systems"):
  with members:
    - "distributed channel synthesis"
    - "achievability proof via output statistics of random binning"
    - "secrecy is cheap if the adversary must reconstruct"
    - "using a secret key to foil an eavesdropper"
    - "multiterminal source coding under logarithmic loss"
    - "a framework for partial secrecy"

Connected through hyperedge HE-5 (induced by source paper "a bit of secrecy for gaussian source compression"):
  with members:
    - "rate distortion theory for secrecy systems"
    - "source channel secrecy with causal disclosure"
    - "secrecy is cheap if the adversary must reconstruct"
    - "using a secret key to foil an eavesdropper"
    - "a framework for partial secrecy"

Question: Please tell me which class the center node 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)