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: "joint beamforming and antenna selection for sum rate maximization in cognitive radio networks"
  Abstract: This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required rates for secondary users, and PUs' interference power constraints. The utility function of interest is nonconcave and the involved constraints are nonconvex, so this problem is hard to solve. Nevertheless, we propose a new iterative algorithm that finds local optima at the least. We use an inner approximation method to construct and solve a simple convex quadratic program of moderate dimension at each iteration of the proposed algorithm. Simulation results indicate that the proposed algorithm converges quickly and outperforms existing approaches.

Connected through hyperedge HE-1 (induced by source paper "cooperative prediction and sensing based spectrum sharing in cognitive radio networks"):
  with members:
    - "an efficient precoder design for multiuser mimo cognitive radio networks with interference constraints"
    - "on the limits of predictability in real world radio spectrum state dynamics from entropy theory to 5g spectrum sharing"
    - "robust cooperative spectrum sensing scheduling optimization in multi channel dynamic spectrum access networks"
    - "primary user traffic classification in dynamic spectrum access networks"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"
    - "robust secure transmission in miso channels based on worst case optimization"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "weighted sum rate optimization for cognitive radio mimo broadcast channels"

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)