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: "throughput maximization in multi channel cognitive radio systems with delay constraints"
  Abstract: We study the throughput-vs-delay trade-off in an overlay multi-channel single-secondary-user cognitive radio system. Due to the limited sensing capabilities of the cognitive radio user, channels are sensed sequentially. Maximizing the throughput in such a problem is well-studied in the literature. Yet, in real-time applications, hard delay constraints need to be considered besides throughput. In this paper, optimal stopping rule and optimal power allocation are discussed to maximize the secondary user's throughput, subject to an average delay constraint. We provide a low complexity approach to the optimal solution of this problem. Simulation results show that this solution allows the secondary user to meet the delay constraint without sacrificing throughput significantly. It also shows the benefits of the optimal power allocation strategy over the constant power allocation strategy.

Connected through hyperedge HE-1 (induced by source paper "scheduling and power allocation to optimize service and queue waiting times in cognitive radio uplinks"):
  with members:
    - "throughput optimization in multichannel cognitive radios with hard deadline constraints"
    - "optimal power cost management using stored energy in data centers"
    - "scheduling heterogeneous real time traffic over fading wireless channels"

Connected through hyperedge HE-2 (induced by source paper "supervised machine learning for signals having rrc shaped pulses"):
  with members:
    - "separation of signals consisting of amplitude and instantaneous frequency rrc pulses using snr uniform training"
    - "dynamic scheduling for delay guarantees for heterogeneous cognitive radio users"

Connected through hyperedge HE-3 (induced by source paper "throughput optimization in multichannel cognitive radios with hard deadline constraints"):
  with members:
    - "optimal power cost management using stored energy in data centers"

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)