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: "performance analysis of cognitive radio systems under qos constraints and channel uncertainty"
  Abstract: In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. #R##N#Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of cognitive radio systems with imperfect channel sensing and estimation"):
  with members:
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "design of learning based mimo cognitive radio systems"
    - "on peak versus average interference power constraints for protecting primary users in cognitive radio networks"
    - "capacity results for block stationary gaussian fading channels with a peak power constraint"

Connected through hyperedge HE-2 (induced by source paper "on the throughput and energy efficiency of cognitive mimo transmissions"):
  with members:
    - "cognitive radio transmission under qos constraints and interference limitations"
    - "optimal spectrum sharing in mimo cognitive radio networks via semidefinite programming"
    - "from spectrum pooling to space pooling opportunistic interference alignment in mimo cognitive networks"
    - "mimo wireless communications under statistical queueing constraints"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "design of learning based mimo cognitive radio systems"
    - "exploiting multi antennas for opportunistic spectrum sharing in cognitive radio networks"
    - "cognitive mimo radio"

Connected through hyperedge HE-3 (induced by source paper "throughput of cognitive radio systems with finite blocklength codes"):
  with members:
    - "the effects of limited channel knowledge on cognitive radio system capacity"
    - "a unified framework for the ergodic capacity of spectrum sharing cognitive radio systems"
    - "throughput analysis of buffer constrained wireless systems in the finite blocklength regime"
    - "optimal power allocation for fading channels in cognitive radio networks ergodic capacity and outage capacity"

Connected through hyperedge HE-4 (induced by source paper "cognitive radio transmission under qos constraints and interference limitations"):
  with members:
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "wideband spectrum sensing in cognitive radio networks"

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)