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 "gan based deep distributional reinforcement learning for resource management in network slicing"):
  Abstract: Network slicing is a key technology in 5G communications system, which aims to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in radio access networks with base stations sharing the same bandwidth. Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands and the allocated bandwidth as the environment state and action, respectively. In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values. Furthermore, we estimate the distributions by approximating a full quantile function, so as to make the training error more controllable. For the sake of protecting the stability of GAN-DDQN's training process from the widely-spanning utility values, we also put forward a reward-clipping mechanism. Finally, we verify the performance of the proposed GAN-DDQN algorithm through extensive simulations.

Includes members:
  Member "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning")
  Member "low complexity distributed radio access network slicing algorithms and experimental results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency")
  Member "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "implicit quantile networks for distributional reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sample based distributional policy gradient")
  Member "distributed distributional deterministic policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust deep reinforcement learning against adversarial perturbations on observations")
  Member "network slicing based 5g and future mobile networks mobility resource management and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "network slicing for 5g with sdn nfv concepts architectures and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future 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.LG(Machine Learning)