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: "sensing throughput tradeoff for interweave cognitive radio system a deployment centric viewpoint"
  Abstract: Secondary access to the licensed spectrum is viable only if the interference is avoided at the primary system. In this regard, different paradigms have been conceptualized in the existing literature. Among these, interweave systems (ISs) that employ spectrum sensing have been widely investigated. Baseline models investigated in the literature characterize the performance of the IS in terms of a sensing-throughput tradeoff, however, this characterization assumes perfect knowledge of the involved channels at the secondary transmitter, which is unavailable in practice. Motivated by this fact, we establish a novel approach that incorporates channel estimation in the system model, and consequently investigate the impact of imperfect channel knowledge on the performance of the IS. More particularly, the variation induced in the detection probability affects the detector’s performance at the secondary transmitter, which may result in severe interference at the primary receivers. In this view, we propose employing average and outage constraints on the detection probability, in order to capture the performance of the IS. Our analysis reveals that with an appropriate choice of the estimation time determined by the proposed approach, the performance degradation of the IS can be effectively controlled, and subsequently the achievable secondary throughput can be significantly enhanced.

Connected through hyperedge HE-1 (induced by source paper "spectral and energy efficiency in cognitive radio systems with unslotted primary users and sensing uncertainty"):
  with members:
    - "sensing throughput tradeoff in cognitive radio with random arrivals and departures of multiple primary users"
    - "primary user traffic estimation for dynamic spectrum access"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"

Connected through hyperedge HE-2 (induced by source paper "on the performance analysis of underlay cognitive radio systems a deployment perspective"):
  with members:
    - "estimation throughput tradeoff for underlay cognitive radio systems"
    - "the effects of limited channel knowledge on cognitive radio system capacity"
    - "a unified framework for the ergodic capacity of spectrum sharing cognitive radio systems"
    - "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"
    - "channel capacity limits of cognitive radio in asymmetric fading environments"

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)