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: "maximizing system throughput by cooperative sensing in cognitive radio networks"
  Abstract: Cognitive Radio Networks allow unlicensed users to opportunistically access the licensed spectrum without causing disruptive interference to the primary users (PUs). One of the main challenges in CRNs is the ability to detect PU transmissions. Recent works have suggested the use of secondary user (SU) cooperation over individual sensing to improve sensing accuracy. In this paper, we consider a CRN consisting of a single PU and multiple SUs to study the problem of maximizing the total expected system throughput. We propose a Bayesian decision rule based algorithm to solve the problem optimally with a constant time complexity. To prioritize PU transmissions, we re-formulate the throughput maximization problem by adding a constraint on the PU throughput. The constrained optimization problem is shown to be NP-hard and solved via a greedy algorithm with pseudo-polynomial time complexity that achieves strictly greater than 1/2 of the optimal solution. We also investigate the case for which a constraint is put on the sensing time overhead, which limits the number of SUs that can participate in cooperative sensing. We reveal that the system throughput is monotonic over the number of SUs chosen for sensing. We illustrate the efficacy of the performance of our algorithms via a numerical investigation.

Connected through hyperedge HE-1 (induced by source paper "energy efficient resource allocation in eh enabled cr networks for iot"):
  with members:
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "wireless energy harvesting in a cognitive relay network"
    - "spatial throughput maximization of wireless powered communication networks"

Connected through hyperedge HE-2 (induced by source paper "joint spectrum allocation and structure optimization in green powered heterogeneous cognitive radio networks"):
  with members:
    - "backhaul aware user association and resource allocation for energy constrained hetnets"
    - "toward hierarchical mobile edge computing an auction based profit maximization approach"
    - "wireless networks with energy harvesting and power transfer joint power and time allocation"
    - "optimal cooperative power allocation for energy harvesting enabled relay networks"
    - "joint spectrum and power allocation for multi node cooperative wireless systems"
    - "on green energy powered cognitive radio networks"
    - "qos aware base station selections for distributed mimo links in broadband wireless networks"

Connected through hyperedge HE-3 (induced by source paper "a multi channel spectrum sensing fusion mechanism for cognitive radio networks design and application to ieee 802 22 wrans"):
  with members:
    - "spectrum sensing in cognitive radios based on multiple cyclic frequencies"

Connected through hyperedge HE-4 (induced by source paper "maximizing social welfare in operator based cognitive radio networks under spectrum uncertainty and sensing inaccuracy"):
  with members:
    - "maximizing system throughput using cooperative sensing in multi channel cognitive radio networks"

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)