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: "on the capacity of 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, showing that the straightforward extension of the K\"orner and Marton inner bound to the investigated scenario is optimal. In particular, this agrees with Nair and Wang's result, which states that the idea of indirect decoding - introduced to improve the 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. Moreover, the joint and individual secrecy capacity regions are established, 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 each cognizant message can be used as a secret key for the other individual message. Further, 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. This was done by showing that principle of indirect decoding introduced by Nair and El Gamal is optimal for this class of channels. This result is in contrast with the nonsecrecy case, where the indirect decoding does not provide any gain.

Connected through hyperedge HE-1 (induced by source paper "secrecy capacity memory tradeoff of erasure broadcast channels"):
  with members:
    - "secure broadcasting using independent secret keys"
    - "benefits of cache assignment on degraded broadcast channels"
    - "cache aided content delivery over erasure broadcast channels"
    - "cache enabled physical layer security for video streaming in backhaul limited cellular networks"
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "noisy broadcast networks with receiver caching"
    - "speeding up future video distribution via channel aware caching aided coded multicast"
    - "on edge caching with secrecy constraints"

Connected through hyperedge HE-2 (induced by source paper "speeding up future video distribution via channel aware caching aided coded multicast"):
  with members:
    - "joint cache channel coding over erasure broadcast channels"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "an efficient coded multicasting scheme preserving the multiplicative caching gain"
    - "finite length analysis of caching aided coded multicasting"
    - "distortion memory tradeoffs in cache aided wireless video delivery"
    - "a unified scheme for two receiver broadcast channels with receiver message side information"
    - "capacity regions for broadcast channels with degraded message sets and message cognition under different secrecy constraints"
    - "an efficient multiple groupcast coded multicasting scheme for finite fractional caching"

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)