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 "the effect of noise correlation in af relay networks"):
  Abstract: In wireless relay networks, noise at the relays can be correlated possibly due to common interference or noise propagation from preceding hops. In this work we consider a parallel relay network with noise correlation. For the relay strategy of amplify-and-forward (AF), we determine the optimal rate maximizing relay gains when correlation knowledge is available at the relays. The effect of correlation on the performance of the relay networks is analyzed for the cases where full knowledge of correlation is available at the relays and when there is no knowledge about the correlation structure. Interestingly we find that, on the average, noise correlation is beneficial regardless of whether the relays know the noise covariance matrix or not. However, the knowledge of correlation can greatly improve the performance. Typically, the performance improvement from correlation knowledge increases with the relay power and the number of relays. With perfect correlation knowledge the system is capable of canceling interference if the number of interferers is less than the number of relays. #R##N#For a dual-hop multiple access parallel network, we obtain closed form expressions for the maximum sum-rate and the optimal relay strategy. The relay optimization for networks with three hops is also considered. For any relay gains for the first stage relays, this represents a parallel relay network with correlated noise. Based on the result of two hop networks with noise correlation, we propose an algorithm for solving the relay optimization problem for three-hop networks.

Includes members:
  Member "network beamforming using relays with perfect channel information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capitalizing backscatter aided hybrid relay communications with wireless energy harvesting")
  Member "optimal relay functionality for snr maximization in memoryless relay networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimized transmission for consensus in wireless sensor networks")

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)