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: "robust layered transmission in secure miso multiuser unicast cognitive radio systems"
  Abstract: This paper studies robust resource-allocation algorithm design for a multiuser multiple-input–single-output (MISO) cognitive radio (CR) downlink communication network. We focus on a secondary system that provides wireless unicast secure layered video information to multiple single-antenna secondary receivers. The resource-allocation algorithm design is formulated as a nonconvex optimization problem for the minimization of the total transmit power at the secondary transmitter. The proposed framework takes into account a quality-of-service (QoS) requirement regarding video communication secrecy in the secondary system, the imperfection of the channel state information (CSI) of potential eavesdroppers (primary receivers) at the secondary transmitter, and a limit for the maximum tolerable received interference power at the primary receivers. Thereby, the proposed problem formulation exploits the    self-protecting   architecture of layered transmission and artificial noise generation to ensure communication secrecy. The considered nonconvex optimization problem is recast as a convex optimization problem via semi-definite programming (SDP) relaxation. It is shown that the global optimal solution of the original problem can be constructed by exploiting both the primal and the dual optimal solutions of the SDP-relaxed problem. In addition, two suboptimal resource-allocation schemes are proposed for the case when the solution of the dual problem is unavailable for constructing the optimal solution. Simulation results demonstrate significant transmit power savings and robustness against CSI imperfection for the proposed optimal and suboptimal resource-allocation algorithms employing layered transmission compared to baseline schemes employing traditional single-layer transmission.

Connected through hyperedge HE-1 (induced by source paper "secure swipt networks based on a non linear energy harvesting model"):
  with members:
    - "secrecy wireless information and power transfer challenges and opportunities"
    - "practical non linear energy harvesting model and resource allocation for swipt systems"
    - "energy efficient resource allocation for wireless powered communication networks"
    - "enhancing wireless information and power transfer by exploiting multi antenna techniques"
    - "multi objective optimization for robust power efficient and secure full duplex wireless communication systems"
    - "robust beamforming for secure communication in systems with wireless information and power transfer"
    - "simultaneous wireless information and power transfer in modern communication systems"
    - "application of smart antenna technologies in simultaneous wireless information and power transfer"

Connected through hyperedge HE-2 (induced by source paper "robust resource allocation for full duplex cognitive radio systems"):
  with members:
    - "robust beamforming for swipt systems with non linear energy harvesting model"
    - "power efficient resource allocation for full duplex radio distributed antenna networks"
    - "multi objective optimization for robust power efficient and secure full duplex wireless communication systems"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "on the spectral efficiency of full duplex small cell wireless systems"
    - "full duplex cooperative cognitive radio with transmit imperfections"
    - "beamforming and rate allocation in miso cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "power efficient multi quality multicast beamforming based on scalable video coding and superposition coding"):
  with members:
    - "multi quality multicast beamforming based on scalable video coding"

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)