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 rotation based method for precoding in gaussian mimome channels"
  Abstract: The problem of maximizing secrecy rate of multiple-input multiple-output multiple-eavesdropper (MIMOME) channels with arbitrary numbers of antennas at each node is studied in this paper. It is proved that linear beamforming is optimum for this problem and optimal signaling to achieve the secrecy capacity is then developed. To this end, it is shown that optimal precoding is a rotation matrix resulted from a set of basic rotations each with one parameter. Next, a gradient-descent based algorithm is developed to find the rotation and power allocation parameters. The proposed rotation-based method can be applied to any MIMOME channels and outperforms state-of-the-art analytical and numerical methods. In particular, the rotation-based precoding achieves higher secrecy rates than the celebrated generalized singular value decomposition (GSVD)-based precoding, with a reasonably higher computational complexity. To further, decrease the computation cost, an algorithm is developed to combine the rotation and GSVD-based precoding. The new rotation-GSVD-based precoding provides an efficient approach to find a near-optimal transmit strategy for the MIMOME channel. Extensive numerical results elaborate on the effectiveness of the rotation-GSVD-based precoding. The new framework developed in this paper can be applied to a verity of similar problems in the context of multi-antenna channels with and without secrecy.

Connected through hyperedge HE-1 (induced by source paper "deep learning based precoding for the mimo gaussian wiretap channel"):
  with members:
    - "interplay between noma and other emerging technologies a survey"
    - "end to end learning of communications systems without a channel model"
    - "deep learning for the gaussian wiretap channel"
    - "deep learning in mobile and wireless networking a survey"
    - "an introduction to deep learning for the physical layer"
    - "optimal beamforming for gaussian mimo wiretap channels with two transmit antennas"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)