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 "capacity regions for broadcast channels with degraded message sets and message cognition under different secrecy constraints"):
  Abstract: This paper considers a three-receiver broadcast channel with degraded message sets and message cognition. The model consists of a common message for all three receivers, a private common message for only two receivers and two additional private messages for these two receivers, such that each receiver is only interested in one message, while being fully cognizant of the other one. First, this model is investigated without any secrecy constraints, where the capacity region is established. This result implies that the straightforward extension of K\"orner and Marton inner bound to the investigated scenario is optimal. In particular, this agrees with Nair and Wang result, which states that the idea of indirect decoding -introduced to improve K\"orner and Marton inner bound- does not provide a better region for this scenario. Further, some secrecy constraints are introduced by letting the private messages to be confidential ones. Two different secrecy criteria are considered: joint secrecy and individual secrecy. For both criteria, a general achievable rate region is provided, in addition to establishing the secrecy capacity regions, if the two legitimate receivers are more capable than the eavesdropper. The established capacity regions indicate that the individual secrecy criterion can provide a larger capacity region as compared to the joint one, because the cognizant messages can be used as a secret keys for the other messages. Moreover, the joint secrecy capacity is established for a more general class of more capable channels, where only one of the two legitimate receivers is more capable than the eavesdropper by proving the optimality of the principle of indirect decoding introduced by Nair and El Gamal for this class of channels. This result is in contrast with the non-secrecy case, where the indirect decoding does not provide any gain.

Includes members:
  Member "individual secrecy for broadcast channels with receiver side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vulnerabilities of massive mimo systems to pilot contamination attacks")
  Member "effective secrecy reliability confusion and stealth":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic regime analysis of noma uplink networks under qos delay constraints")
  Member "strong secrecy from channel resolvability":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "securing distributed function approximation via coding for continuous compound channels")
  Member "broadcast capacity region of two phase bidirectional relaying":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the dof of two way 2 2 2 relay networks with or without relay caching")
  Member "multiple access channels with generalized feedback and confidential messages":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiple access wiretap channel with noiseless feedback")
  Member "the gaussian multiple access wire tap channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an achievable region for the multiple access wiretap channels with confidential and open messages")
  Member "shannon s secrecy system with informed receivers and its application to systematic coding for wiretapped channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure broadcasting using independent secret keys")

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)