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 "dynamic spectrum access with statistical qos provisioning a distributed learning approach beyond expectation optimization"):
  Abstract: This article investigates the problem of dynamic spectrum access with statistical quality of service (QoS) provisioning for dynamic canonical networks, in which the channel states are time-varying from slot to slot. In the existing work with time-varying environment, the commonly used optimization objective is to maximize the expectation of a certain metric (e.g., throughput or achievable rate). However, it is realized that expectation alone is not enough since some applications are sensitive to the channel fluctuations. Effective capacity is a promising metric for time-varying service process since it characterizes the packet delay violating probability (regarded as an important statistical QoS index), by taking into account not only the expectation but also other high-order statistic. We formulate the interactions among the users in the time-varying environment as a non-cooperative game, in which the utility function is defined as the achieved effective capacity. We prove that it is an ordinal potential game which has at least one pure strategy Nash equilibrium. In addition, we propose a multi-agent learning algorithm which is proved to achieve stable solutions with uncertain, dynamic and incomplete information constraints. The convergence of the proposed learning algorithm is verified by simulation results. Also, it is shown that the proposed multi-agent learning algorithm achieves satisfactory performance.

Includes members:
  Member "coalitional games in partition form for joint spectrum sensing and access in cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shipper cooperation in stochastic drone delivery a dynamic bayesian game approach")
  Member "quality of service provisioning in decentralized networks a satisfaction equilibrium approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "evolutionarily stable spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic model for network selection in next generation hetnets with memory affecting rational users")
  Member "multi agent q learning of channel selection in multi user cognitive radio systems a two by two case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "effective capacity analysis of cognitive radio channels for quality of service provisioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the effective capacity of an underwater acoustic channel under impersonation attack")
  Member "adaptive channel allocation spectrum etiquette for cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges")

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)