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 "ran slicing in a uav network for embb service provision"):
  Abstract: This paper is concerned with a problem of radio access network (RAN) slicing in a unmanned aerial vehicle (UAV) network for enhanced mobile broadband (eMBB) service provision. Unlike the literature focusing on RAN resource sharing in a UAV network, this paper simultaneously considers the enforcement of network slicing and the share of RAN resources. Specifically, this paper formulates the problem of RAN slicing in a UAV network as a time sequence optimization problem with a goal of providing an energy-efficient and fair eMBB service under constraints of both eMBB user equipments' quality of service requirements and UAVs' energy consumption and trajectories. This problem is a mixed-integer-non-convex-programming problem that is highly challenging to mitigate. As such, this paper proposes a framework that needs to repeatedly and separately enforce network slicing and optimize RAN resource allocation for mitigating it by exploring the Lyapunov optimization. Besides, this paper leverages a successive convex approximate method to transform the non-convex RAN resource optimization problem into an approximate convex optimization one. Simulation results show that the proposed framework can achieve significant performance gains as compared with other benchmark algorithms.

Includes members:
  Member "embb urllc resource slicing a risk sensitive approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligent resource slicing for embb and urllc coexistence in 5g and beyond a deep reinforcement learning based approach")
  Member "a utility driven multi queue admission control solution for network slicing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning for network slicing resource management a comprehensive survey")
  Member "5g wireless network slicing for embb urllc and mmtc a communication theoretic view":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overhead aware design of reconfigurable intelligent surfaces in smart radio environments")
  Member "network slicing based 5g and future mobile networks mobility resource management and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "network slicing to enable scalability and flexibility in 5g mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "a lyapunov optimization approach to repeated stochastic games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ultra reliable and low latency wireless communication tail risk and scale")

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)