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: "reinforcement learning for resource provisioning in the vehicular cloud"
  Abstract: This article presents a concise view of vehicular clouds that incorporates various vehicular cloud models that have been proposed to date. Essentially, they all extend the traditional cloud and its utility computing functionalities across the entities in the vehicular ad hoc network. These entities include fixed roadside units, onboard units embedded in the vehicle, and personal smart devices of drivers and passengers. Cumulatively, these entities yield abundant processing, storage, sensing, and communication resources. However, vehicular clouds require novel resource provisioning techniques that can address the intrinsic challenges of dynamic demands for the resources and stringent QoS requirements. In this article, we show the benefits of reinforcement-learning-based techniques for resource provisioning in the vehicular cloud. The learning techniques can perceive long-term benefits and are ideal for minimizing the overhead of resource provisioning for vehicular clouds.

Connected through hyperedge HE-1 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-2 (induced by source paper "toward intelligent vehicular networks a machine learning framework"):
  with members:
    - "mmwave beam prediction with situational awareness a machine learning approach"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "machine learning for vehicular networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "inverse multipath fingerprinting for millimeter wave v2i beam alignment"
    - "vehicular communications a network layer perspective"

Connected through hyperedge HE-3 (induced by source paper "a survey on resource allocation in vehicular networks"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "radio resource allocation for reliable out of coverage v2v communications"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "deep reinforcement learning a brief survey"
    - "vehicular communications a network layer perspective"
    - "an end to end network slicing framework for 5g wireless communication systems"
    - "coding based data broadcasting for time critical applications with rate adaptation"
    - "dynamic resource allocation in cognitive radio networks"

Connected through hyperedge HE-4 (induced by source paper "spectrum sharing in vehicular networks based on multi agent reinforcement learning"):
  with members:
    - "toward intelligent vehicular networks a machine learning framework"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "vehicular communications a network layer perspective"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "energy efficient resource allocation in vehicular cloud based architecture"):
  with members:
    - "big data analytics for wireless and wired network design a survey"
    - "patient centric cellular networks optimization using big data analytics"
    - "bounds for energy efficient survivable ip over wdm networks with network coding"
    - "optimized energy aware 5g network function virtualization"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for autonomous defence in software defined networking"):
  with members:
    - "semantic adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning based resource allocation for v2v communications"):
  with members:
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "machine learning for vehicular networks"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "vehicular communications a network layer perspective"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "dynamic proximity aware resource allocation in vehicle to vehicle v2v communications"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning based qos qoe aware service function chaining in software driven 5g slices"):
  with members:
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "nfv and sdn key technology enablers for 5g networks"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "playing atari with 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)