human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
  Abstract: Network slicing promises to provision diversified services with distinct requirements in one infrastructure. Deep reinforcement learning (e.g., deep $\mathcal{Q}$-learning, DQL) is assumed to be an appropriate algorithm to solve the demand-aware inter-slice resource management issue in network slicing by regarding the varying demands and the allocated bandwidth as the environment state and the action, respectively. However, allocating bandwidth in a finer resolution usually implies larger action space, and unfortunately DQL fails to quickly converge in this case. In this paper, we introduce discrete normalized advantage functions (DNAF) into DQL, by separating the $\mathcal{Q}$-value function as a state-value function term and an advantage term and exploiting a deterministic policy gradient descent (DPGD) algorithm to avoid the unnecessary calculation of $\mathcal{Q}$-value for every state-action pair. Furthermore, as DPGD only works in continuous action space, we embed a k-nearest neighbor algorithm into DQL to quickly find a valid action in the discrete space nearest to the DPGD output. Finally, we verify the faster convergence of the DNAF-based DQL through extensive simulations.

Connected through hyperedge HE-1 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "machine learning in the air"
    - "deep reinforcement learning for resource management in network slicing"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "auto scaling network resources using machine learning to improve qos and reduce cost"
    - "addressing function approximation error in actor critic methods"
    - "deep learning in mobile and wireless networking a survey"
    - "comparative study of virtual machines and containers for devops developers"

Connected through hyperedge HE-2 (induced by source paper "gan based deep distributional reinforcement learning for resource management in network slicing"):
  with members:
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "slice as an evolutionary service genetic optimization for inter slice resource management in 5g networks"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "a distributional perspective on reinforcement learning"

Question: Please tell me which class the center node 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)