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 "what is needed to exploit knowledge of primary transmissions"):
  Abstract: Recently, Tarokh and others have raised the possibility that a cognitive radio might know the interference signal being transmitted by a strong primary user in a non-causal way, and use this knowledge to increase its data rates. However, there is a subtle difference between knowing the signal transmitted by the primary and the actual interference at our receiver since there is a wireless channel between these two points. We show that even an unknown phase results in a substantial decrease in the data rates that can be achieved, and thus there is a need to feedback interference channel estimates to the cognitive transmitter. We then consider the case of fading channels. We derive an upper bound on the rate for given outage error probability for faded dirt. We give a scheme that uses appropriate "training" to obtain such estimates and quantify this scheme's required overhead as a function of the relevant coherence time and interference power.

Includes members:
  Member "cognitive radio an information theoretic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cognitive radio networks an information theoretic perspective")
  Member "duality between channel capacity and rate distortion with two sided state information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variable length source dispersions differ under maximum and average error criteria")
  Member "carbon copying onto dirty paper":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the capacity of the carbon copy onto dirty paper channel")

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)