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: "a cooperative bayesian nonparametric framework for primary user activity monitoring in cognitive radio networks"
  Abstract: This paper introduces a novel approach that enables a number of cognitive radio devices that are observing the availability pattern of a number of primary users (PUs), to cooperate and use Bayesian nonparametric techniques to estimate the distributions of the PUs' activity pattern. To address this problem, a coalitional game is formulated between the cognitive devices and an algorithm for cooperative coalition formation is proposed. It is shown that the proposed coalition formation algorithm allows the cognitive nodes that are experiencing a similar behavior from some PUs to self-organize into disjoint, independent coalitions. Inside each coalition, the cooperative cognitive nodes use Bayesian nonparametric techniques so as to improve the accuracy of the estimated PUs' activity distributions. Simulation results show that the proposed algorithm significantly improves the estimates of the PUs' activity patterns.

Connected through hyperedge HE-1 (induced by source paper "artificial intelligence based cognitive routing for cognitive radio networks"):
  with members:
    - "multicasting in cognitive radio networks"
    - "genetic algorithms in wireless networking techniques applications and issues"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a coalitional game for distributed inference in sensor networks with dependent observations"):
  with members:
    - "robust distributed maximum likelihood estimation with dependent quantized data"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "a generic approach to coalition formation"
    - "estimation diversity and energy efficiency in distributed sensing"
    - "stable partitions in coalitional games"

Connected through hyperedge HE-3 (induced by source paper "wireless information and power transfer in cooperative networks with spatially random relays"):
  with members:
    - "power allocation strategies in energy harvesting wireless cooperative networks"
    - "secrecy wireless information and power transfer with miso beamforming"
    - "opportunistic wireless energy harvesting in cognitive radio networks"
    - "simultaneous information and power transfer for broadband wireless systems"
    - "relaying protocols for wireless energy harvesting and information processing"
    - "wireless information and power transfer architecture design and rate energy tradeoff"
    - "mimo broadcasting for simultaneous wireless information and power transfer"

Connected through hyperedge HE-4 (induced by source paper "primary user traffic estimation for dynamic spectrum access"):
  with members:
    - "exploiting channel correlation and pu traffic memory for opportunistic spectrum scheduling"
    - "performance of joint spectrum sensing and mac algorithms for multichannel opportunistic spectrum access ad hoc networks"
    - "throughput and collision analysis of multichannel multistage spectrum sensing algorithms"
    - "sensitive white space detection with spectral covariance sensing"

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)