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: "on the capacity of wireless powered cognitive relay network with interference alignment"
  Abstract: In this paper, a two-hop decode-and-forward cognitive radio system with deployed interference alignment is considered. The relay node is energy- constrained and scavenges the energy from the interference signals. In the literature, there are two main energy harvesting protocols, namely, time-switching relaying and power-splitting relaying. We first demonstrate how to design the beamforming matrices for the considered primary and secondary networks. Then, the system capacity under perfect and imperfect channel state information scenarios, considering different portions of time-switching and power-splitting protocols, is estimated.

Connected through hyperedge HE-1 (induced by source paper "on the feasibility of interference alignment in compounded mimo broadcast channels with antenna correlation and mixed user classes"):
  with members:
    - "error performance of wireless powered cognitive relay networks with interference alignment"
    - "feasibility of interference alignment for the mimo interference channel"
    - "bounds on eigenvalues of a spatial correlation matrix"
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "why does the kronecker model result in misleading capacity estimates"
    - "on feasibility of interference alignment in mimo interference networks"
    - "interference alignment with analog channel state feedback"
    - "on feasibility of interference alignment in mimo interference networks"

Connected through hyperedge HE-2 (induced by source paper "ergodic capacity analysis of wireless powered af relaying systems over alpha µ fading channels"):
  with members:
    - "error performance of wireless powered cognitive relay networks with interference alignment"
    - "simultaneous information and power transfer for broadband wireless systems"
    - "relaying protocols for wireless energy harvesting and information processing"
    - "wireless information and power transfer architecture design and rate energy tradeoff"

Connected through hyperedge HE-3 (induced by source paper "error performance of wireless powered cognitive relay networks with interference alignment"):
  with members:
    - "ergodic capacity analysis of wireless powered af relaying systems over alpha µ fading channels"
    - "information and energy cooperation in cognitive radio networks"
    - "relaying protocols for wireless energy harvesting and information processing"

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)