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 "wiretap and gelfand pinsker channels analogy and its applications"):
  Abstract: A framework of analogy between wiretap channels (WTCs) and state-dependent point-to-point channels with non-causal encoder channel state information (referred to as Gelfand-Pinker channels (GPCs)) is proposed. A good (reliable and secure) sequence of wiretap codes is shown to induce a good (reliable) sequence of codes for a corresponding GPC. Consequently, the framework enables exploiting existing results for GPCs to produce converse proofs for their wiretap analogs. The fundamental limits of communication of analogous wiretap and GP models are characterized by the same rate bounds; the optimization domains may differ. The analogy readily extends to multiuser broadcasting scenarios, encompassing broadcast channels (BCs) with deterministic components, degradation ordering between users, and BCs with cooperative receivers. Given a wiretap BC (WTBC) with two receivers and one eavesdropper, an analogous Gelfand-Pinsker BC (GPBC) is constructed by converting the eavesdropper's observations to a state sequence with an appropriate product distribution, and non-causally revealing the states to the encoder. The transition matrix of the (state-dependent) GPBC is the appropriate conditional marginal of the WTBC's transition law, with the eavesdropper's output playing the role of the channel state. Past capacity results for the semi-deterministic (SD) GPBC and the physically-degraded (PD) GPBC with an informed receiver are leveraged to furnish analogy-based converse proofs for the analogous WTBC setups. This characterizes the secrecy-capacity regions of the SD-WTBC, which was an open problem until this work, as well as the PD-WTBC where the stronger receiver also observes the eavesdropper's channel output. These new derivations exemplify the strength of the wiretap-GP analogy as a research tool through which results on one problem directly translate into advances in the study of the other.

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 "wiretap channels with random states non causally available at the encoder":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "keyless covert communication via channel state information")
  Member "capacity of the state dependent wiretap channel secure writing on dirty paper":
    also connected through other hyperedges:
      (none)
  Member "analogy between gambling and measurement based work extraction":
    also connected through other hyperedges:
      (none)
  Member "duality of a source coding problem and the semi deterministic broadcast channel with rate limited cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strong secrecy for cooperative broadcast channels")
  Member "semantic security capacity for wiretap channels of type ii":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explicit codes for the wiretap channel a unified design framework")
  Member "secrecy capacity region of some classes of wiretap broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secrecy rates for a channel with two senders and two receivers")
  Member "on state dependent broadcast channels with cooperation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strong secrecy for cooperative broadcast channels")

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)