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 "superposition coding based bounds and capacity for the cognitive z interference channels"):
  Abstract: This paper considers the cognitive interference channel (CIC) with two transmitters and two receivers, in which the cognitive transmitter non-causally knows the message and codeword of the primary transmitter. We first introduce a discrete memoryless more capable CIC, which is an extension to the more capable broadcast channel (BC). Using superposition coding, we propose an inner bound and an outer bound on its capacity region. The outer bound is also valid when the primary user is under strong interference. For the Gaussian CIC, this outer bound applies for $|a| \geq 1 $, where $a$ is the gain of interference link from secondary user to primary receiver. These capacity inner and outer bounds are then applied to the Gaussian cognitive Z-interference channel (GCZIC) where only the primary receiver suffers interference. Upon showing that jointly Gaussian input maximizes these bounds for the GCZIC, we evaluate the bounds for this channel. The new outer bound is strictly tighter than other outer bounds on the capacity of the GCZIC at strong interference ($a^2 \geq 1 $). Especially, the outer bound coincides with the inner bound for $|a| \geq \sqrt{1 + P_1}$ and thus, establishes the capacity of the GCZIC at this range. For such a large $a$, superposition encoding at the cognitive transmitter and successive decoding at the primary receiver are capacity-achieving.

Includes members:
  Member "inner and outer bounds for the gaussian cognitive interference channel and new capacity results":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cognitive radio networks an information theoretic perspective")
  Member "new results on the capacity of the gaussian cognitive interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiuser cognitive radio networks an information theoretic perspective")
  Member "on the capacity of a class of cognitive z interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cognitive radio networks an information theoretic perspective")
  Member "cognitive radio an information theoretic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cognitive radio networks an information theoretic perspective")

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)