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 "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"):
  Abstract: To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.

Includes members:
  Member "multipath transmission scheduling in millimeter wave cloud radio access networks":
    also connected through other hyperedges:
      (none)
  Member "performance optimization in mobile edge computing via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "online learning for offloading and autoscaling in energy harvesting mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "mobile edge computing a survey on architecture and computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "latency and reliability aware task offloading and resource allocation for mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "joint offloading and computing optimization in wireless powered mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "delay optimal computation task scheduling for mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "analysis on computation intensive status update in mobile edge computing")

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.LG(Machine Learning)