human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust cooperative spectrum sensing scheduling optimization in multi channel dynamic spectrum access networks"):
  Abstract: Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity.

Includes members:
  Member "primary user traffic estimation for dynamic spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral and energy efficiency in cognitive radio systems with unslotted primary users and sensing uncertainty")
  Member "distributed spectrum sensing with sequential ordered transmissions to a cognitive fusion center":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)