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: "spectrum sharing multi hop cooperative relaying performance analysis using extreme value theory"
  Abstract: In spectrum-sharing cognitive radio systems, the transmit power of secondary users has to be very low due to the restrictions on the tolerable interference power dictated by primary users. In order to extend the coverage area of secondary transmission and reduce the corresponding interference region, multi-hop amplify-and-forward (AF) relaying can be implemented for the communication between secondary transmitters and receivers. This paper addresses the fundamental limits of this promising technique. Specifically, the effect of major system parameters on the performance of spectrum-sharing multi-hop AF relaying is investigated. To this end, the optimal transmit power allocation at each node along the multi-hop link is firstly addressed. Then, the extreme value theory is exploited to study the limiting distribution functions of the lower and upper bounds on the end-to-end signal-to-noise ratio of the relaying path. Our results disclose that the diversity gain of the multi-hop link is always unity, regardless of the number of relaying hops. On the other hand, the coding gain is proportional to the water level of the optimal water-filling power allocation at secondary transmitter and to the large-scale path-loss ratio of the desired link to the interference link at each hop, yet is inversely proportional to the accumulated noise, i.e. the product of the number of relays and the noise variance, at the destination. These important findings do not only shed light on the performance of the secondary transmissions but also benefit system designers improving the efficiency of future spectrum-sharing cooperative systems.

Connected through hyperedge HE-1 (induced by source paper "transmit power policy and ergodic multicast rate analysis of cognitive radio networks in generalized fading"):
  with members:
    - "asymptotic performance analysis of generalized user selection for interference limited multiuser secondary networks"
    - "asymptotic maximal order statistic for sir in κ μ shadowed fading"
    - "mimo underlay cognitive radio optimized power allocation effective number of transmit antennas and harvest transmit tradeoff"
    - "error vector magnitude analysis in generalized fading with co channel interference"
    - "outage probability and rate for kappa mu shadowed fading in interference limited scenario"
    - "coverage analysis in downlink poisson cellular network with kappa mu shadowed fading"
    - "relay selection improves the security reliability trade off in cognitive radio systems"
    - "the effects of limited channel knowledge on cognitive radio system capacity"

Connected through hyperedge HE-2 (induced by source paper "asymptotic maximal order statistic for sir in κ μ shadowed fading"):
  with members:
    - "error vector magnitude analysis in generalized fading with co channel interference"
    - "outage probability and rate for kappa mu shadowed fading in interference limited scenario"
    - "coverage analysis in downlink poisson cellular network with kappa mu shadowed fading"
    - "massive mimo ten myths and one critical question"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "massive mimo performance evaluation based on measured propagation data"
    - "massive mimo for next generation wireless systems"
    - "on the sum of correlated squared kappa mu shadowed random variables and its application to performance analysis of mrc"

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)