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: "multicasting in cognitive radio networks"
  Abstract: Multicasting is a fundamental networking primitive utilized by numerous applications. This also holds true for cognitive radio networks which have been proposed as a solution to the problems that emanate from the static non-adaptive features of classical wireless networks. A prime application of cognitive radio networks is dynamic spectrum access, which improves the efficiency of spectrum allocation by allowing a secondary network, comprising secondary users, to share spectrum licensed to a primary licensed network comprising primary users. Multicasting in cognitive radio networks is a challenging problem due to the dynamic nature of spectrum opportunities available to the secondary users. Various approaches, including those based on optimization theory, network coding, algorithms, have been proposed for performing efficient multicast in cognitive radio networks. In this paper, we provide a self-contained tutorial on algorithms and techniques useful for solving the multicast problem, and then provide a comprehensive survey of protocols that have been proposed for multicasting in cognitive radio networks. We conclude this paper by identifying open research questions and future research directions.

Connected through hyperedge HE-1 (induced by source paper "on green multicasting over cognitive radio fading channels"):
  with members:
    - "energy efficient transmission for multicast services in miso distributed antenna systems"
    - "on green energy powered cognitive radio networks"

Connected through hyperedge HE-2 (induced by source paper "artificial intelligence based cognitive routing for cognitive radio networks"):
  with members:
    - "genetic algorithms in wireless networking techniques applications and issues"
    - "a cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks"
    - "dynamic interference minimization routing game for on demand cognitive pilot channel"
    - "beyond nash equilibrium solution concepts for the 21st century"
    - "energy efficient resource allocation in wireless networks"
    - "reinforcement learning for adaptive routing"
    - "reinforcement learning a survey"

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)