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 for resource management in network slicing"
  Abstract: Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.

Connected through hyperedge HE-1 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning"):
  with members:
    - "ieee 802 11be extremely high throughput the next generation of wi fi technology beyond 802 11ax"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "experience driven networking a deep reinforcement learning based approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "progressive neural architecture search"
    - "hindsight experience replay"
    - "a deep reinforcement learning based framework for content caching"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "management and orchestration of virtual network functions via deep reinforcement learning"):
  with members:
    - "deep reinforcement learning with discrete normalized advantage functions for resource management in network slicing"
    - "machine learning in the air"
    - "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-3 (induced by source paper "application of machine learning in wireless networks key techniques and open issues"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "learning to optimize training deep neural networks for interference management"
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep learning for radio resource allocation in multi cell networks"
    - "multi tenant cross slice resource orchestration a deep reinforcement learning approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep learning in mobile and wireless networking a survey"
    - "handover control in wireless systems via asynchronous multi user deep 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.NI(Networking and Internet Architecture)