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: "cognitive radio transmission strategies for primary markovian channels"
  Abstract: A fundamental problem in cognitive radio systems is that the cognitive radio is ignorant of the primary channel state and, hence, of the amount of actual harm it inflicts on the primary license holder. Sensing the primary transmitter does not help in this regard. To tackle this issue, we assume in this paper that the cognitive user can eavesdrop on the ACK/NACK Automatic Repeat reQuest (ARQ) fed back from the primary receiver to the primary transmitter. Assuming a primary channel state that follows a Markov chain, this feedback gives the cognitive radio an indication of the primary link quality. Based on the ACK/NACK received, we devise optimal transmission strategies for the cognitive radio so as to maximize a weighted sum of primary and secondary throughput. The actual weight used during network operation is determined by the degree of protection afforded to the primary link. We begin by formulating the problem for a channel with a general number of states. We then study a two-state model where we characterize a scheme that spans the boundary of the primary-secondary rate region. Moreover, we study a three-state model where we derive the optimal strategy using dynamic programming. We also extend our two-state model to a two-channel case, where the secondary user can decide to transmit on a particular channel or not to transmit at all. We provide numerical results for our optimal strategies and compare them with simple greedy algorithms for a range of primary channel parameters. Finally, we investigate the case where some of the parameters are unknown and are learned using hidden Markov models (HMM).

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.NI(Networking and Internet Architecture)