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 based mode selection and resource management for green fog radio access networks"
  Abstract: Fog radio access networks (F-RANs) are seen as potential architectures to support services of internet of things by leveraging edge caching and edge computing. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. Motivated by the recent development of artificial intelligence, a deep reinforcement learning (DRL) based joint mode selection and resource management approach is proposed. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors' on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. By simulations, the impacts of several parameters, such as learning rate and edge caching service capability, on system performance are demonstrated, and meanwhile the proposal is compared with other different schemes to show its effectiveness. Moreover, transfer learning is integrated with DRL to accelerate learning process.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "federated learning strategies for improving communication efficiency"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "network slicing in fog radio access networks issues and challenges"
    - "communication efficient learning of deep networks from decentralized data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-3 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for adaptive caching in hierarchical content delivery networks"):
  with members:
    - "joint switch upgrade and controller deployment in hybrid software defined networks"
    - "bandit online learning with unknown delays"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "decentralized caching for content delivery based on blockchain a game theoretic perspective"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning a brief survey"
    - "a deep reinforcement learning based framework for content caching"

Connected through hyperedge HE-5 (induced by source paper "learn to allocate resources in vehicular networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "vehicular communications a network layer perspective"
    - "dynamic proximity aware resource allocation in vehicle to vehicle v2v communications"
    - "an overview of gradient descent optimization algorithms"
    - "deep reinforcement learning with double q learning"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-6 (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"
    - "deep reinforcement learning for resource management in network slicing"
    - "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 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)