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 "joint spectrum reservation and on demand request for mobile virtual network operators"):
  Abstract: With wireless network virtualization, Mobile Virtual Network Operators (MVNOs) can develop new services on a low-cost platform by leasing virtual resources from mobile network owners. In this paper, we investigate a two-stage spectrum leasing framework, where an MVNO acquires radio spectrum through both advance reservation and on-demand request. To maximize its surplus, the MVNO jointly optimizes the amount of spectrum to lease in the two stages by taking into account the traffic distribution, random user locations, wireless channel statistics, Quality of Service (QoS) requirements, and the prices differences. Meanwhile, the acquired spectrum resources are dynamically allocated to the MVNO's mobile subscribers (users) according to fast channel fadings in order to maximize the utilization of the resources. The MVNO's surplus maximization problem is naturally formulated as a tri-level nested optimization problem that consists of Dynamic Resource Allocation (DRA), on-demand request, and advance reservation subproblems. To solve the problem efficiently, we rigorously analyze the structure of the optimal solution in the DRA problem, and the optimal value is used to find the optimal leasing decisions in the two stages. In particular, we derive closed-form expressions of the optimal advance reservation and on-demand requests when the proportional fair utility function is adopted. We further extend the analysis to general utility functions and derive a Stochastic Gradient Decent (SGD) algorithm to find the optimal leasing decisions. Simulation results show that the two-stage spectrum leasing strategy can take advantage of both the price discount of advance reservation and the flexibility of on-demand request to deal with traffic variations.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "from network sharing to multi tenancy the 5g network slice broker":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")
  Member "virtualization of 5g cellular networks as a hierarchical combinatorial auction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "iris deep reinforcement learning driven shared spectrum access architecture for indoor neutral host small cells")
  Member "distributed virtual resource allocation in small cell networks with full duplex self backhauls and virtualization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "defining and surveying wireless link virtualization and wireless network virtualization")

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.NI(Networking and Internet Architecture)