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: "the capacity of mixed and one sided gaussian interference channels"
  Abstract: This paper adds to the understanding of the capacity region of the Gaussian interference channel. To this end, the capacity region of the one-sided Gaussian interference channel is first fully characterized. This is accomplished by introducing a new representation of the Han-Kobayashi region and providing a new outer bound on the capacity region of this channel which is tight both in the weak and strong interference regimes. In light of this capacity result, the capacity region of the degraded Gaussian interference channel is established immediately. Next, by combining the capacity regions of the one-sided interference channels in the weak and strong interference regimes, new outer bounds on the capacity region of the interference channel are introduced, in various interference regimes. It is then proved that the outer bound corresponding to the mixed interference regime, in which one of the receivers is subject to strong interference while the other one suffers from weak interference, is tight in a broad range of this regime. The new capacity results, on the whole, confirm the optimality of decoding part of the interference and treating the rest as noise. The optimum amount to be decoded varies from 0 to 100% of the interfering signal, depending on the relative importance of the users' rates (i.e., the ratio of weights in the weighted sum-rate), their transmission powers, and the gain of the weak interference link. Optimal values are found explicitly, based on the above parameters.

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)