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 "secret key generation with one communicator and a one shot converse via hypercontractivity"):
  Abstract: A new model of multi-party secret key agreement is proposed, in which one terminal called the communicator can transmit public messages to other terminals before all terminals agree on a secret key. A single-letter characterization of the achievable region is derived in the stationary memoryless case. The new model generalizes some other (old and new) models of key agreement. In particular, key generation with an omniscient helper is the special case where the communicator knows all sources, for which we derive a zero-rate one-shot converse for the secret key per bit of communication.

Includes members:
  Member "the likelihood encoder for lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyless covert communication via channel state information")
  Member "secret key agreement general capacity and second order asymptotics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a capacity achieving one message key agreement with finite blocklength analysis")
  Member "assisted common information with an application to secure two party sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication for generating correlation a unifying survey")
  Member "the likelihood encoder for lossy source compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the likelihood encoder for lossy compression")
  Member "distributed channel synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")

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)