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: "capacity and algorithms for a cognitive network with primary secondary user cooperation"
  Abstract: In this work, we examine cognitive radio networks, where secondary users may act as relays for messages sent by the primary user, hence offering performance improvement of primary transmissions, while at the same time obtaining more transmission opportunities for their own data. In particular, assuming the broadcast packet erasure model with feedback, we investigate the capacity of the fundamental cooperative cognitive radio network which consists of one primary and one secondary transmitter-receiver pairs. The primary transmitter is the owner of the channel and as such, we intend to keep its operations simple and to avoid increasing its storage requirements. Specifically, the primary transmitter does not receive data sent by the secondary transmitter and does not perform any coding operations. On the other hand, the secondary transmitter can overhear primary transmissions and is allowed to perform any coding operations. We develop an outer bound to the capacity of the fundamental cooperative cognitive radio network under consideration. Then, we propose a coding-scheduling algorithm suitable for this type of networks, which involves only XOR network coding operations. The complexity of the scheduling decisions of the proposed algorithm depends on the channel statistical parameters and three cases, depending on the relations between channel erasure probabilities, are distinguished. For the first two cases the rate region of the proposed algorithm coincides with the developed capacity outer bound, hence the algorithm is capacity achieving. For the third case, the rate region of the proposed algorithm is not identical to the outer bound; however, numerical results show that it is fairly close to the derived outer bound for a wide range of the statistical parameters of the system.

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)