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 "on the optimality of secure communication without using cooperative jamming"):
  Abstract: We consider secure communication over a two-user Gaussian interference channel, where each transmitter sends a confidential message to its legitimate receiver. For this setting, we identify a regime where the simple scheme of using Gaussian wiretap codebook at each transmitter (without cooperative jamming) and treating interference as noise at each intended receiver (in short, GWC-TIN scheme) achieves the optimal secure sum capacity to within a constant gap. The results are proved by first considering the deterministic interference channel model and identifying a regime in which a simple scheme without using cooperative jamming is optimal in terms of secure sum capacity. For the symmetric case of the deterministic model, this simple scheme is optimal if and only if the interference-to-signal ratio (in channel strengths) is no more than 2/3.

Includes members:
  Member "secure degrees of freedom of one hop wireless networks with no eavesdropper csit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adding common randomness can remove the secrecy constraints in communication networks")
  Member "secure degrees of freedom of one hop wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  Member "principles of physical layer security in multiuser wireless networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "angle aware user cooperation for secure massive mimo in rician fading channel")
  Member "interference alignment for secrecy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adding common randomness can remove the secrecy constraints in communication networks")
  Member "discrete memoryless interference and broadcast channels with confidential messages secrecy rate regions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cognitive radio networks an information theoretic perspective")
  Member "gaussian interference channel capacity to within one bit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fundamental limits of wireless caching under mixed cacheable and uncacheable traffic")

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)