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: "an efficient precoder design for multiuser mimo cognitive radio networks with interference constraints"
  Abstract: We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output (MIMO) broadcast channel, where the secondary system consisting of a secondary base station (BS) and a group of secondary users is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing (ZF) method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex–concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "a practical spectrum sharing scheme for cognitive radio networks design and experiments"):
  with members:
    - "nonorthogonal multiple access in large scale underlay cognitive radio networks"
    - "coordinated shared spectrum precoding with distributed csit"
    - "interference mitigation for cognitive radio mimo systems based on practical precoding"
    - "cognitive beamforming made practical effective interference channel and learning throughput tradeoff"

Connected through hyperedge HE-2 (induced by source paper "cooperative prediction and sensing based spectrum sharing in cognitive radio networks"):
  with members:
    - "joint beamforming and antenna selection for sum rate maximization in cognitive radio networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "joint beamforming and antenna selection for sum rate maximization in cognitive radio networks"):
  with members:
    - "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)