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: "vehicular communications a network layer perspective"
  Abstract: Vehicular communications, referring to information exchange among vehicles, pedestrians, and infrastructures, have become very popular and been widely studied recently due to its great potential to support intelligent transportation and various safety applications. Via vehicular communications, manually driving vehicles and autonomous vehicles can collect useful information to improve traffic safety and support infotainment services. In this paper, we provide a comprehensive overview of recent research on enabling efficient vehicular communications from the network layer perspective. First, we introduce general applications and unique characteristics of vehicular networks and the corresponding classifications. Based on different driving patterns of vehicles, we divide vehicular networks into two categories, i.e., manually driving vehicular networks and automated driving vehicular networks, and then discuss the available communication techniques, network structures, routing protocols, and handoff strategies applied in these vehicular networks. Finally, we identify the challenges confronted by the current vehicular communications and present the corresponding research opportunities.

Connected through hyperedge HE-1 (induced by source paper "a virtual network customization framework for multicast services in nfv enabled core networks"):
  with members:
    - "air ground integrated vehicular network slicing with content pushing and caching"
    - "service overlay forest embedding for software defined cloud networks"
    - "network slicing for service oriented networks under resource constraints"
    - "distributed service function chaining"
    - "service function chaining resource allocation a survey"
    - "scalable steiner tree for multicast communications in software defined networking"
    - "specifying and placing chains of virtual network functions"

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"
    - "millimeter wave vehicular communication to support massive automotive sensing"

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"
    - "an end to end network slicing framework for 5g wireless communication systems"
    - "reinforcement learning for resource provisioning in the vehicular cloud"
    - "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 "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"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "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-5 (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"
    - "reinforcement learning for resource provisioning in the vehicular cloud"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "spectrum resource management for multi access edge computing in autonomous vehicular networks"):
  with members:
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "business case and technology analysis for 5g low latency applications"
    - "optimal distributed control for platooning via sparse coprime factorizations"
    - "user association for load balancing in heterogeneous cellular networks"

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"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "dynamic proximity aware resource allocation in vehicle to vehicle v2v communications"
    - "reinforcement learning for resource provisioning in the vehicular cloud"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "resource allocation for low latency vehicular communications with packet retransmission"):
  with members:
    - "stable throughput and delay analysis of a random access network with queue aware transmission"
    - "low latency millimeter wave communications traffic dispersion or network densification"
    - "coding versus arq in fading channels how reliable should the phy be"

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.CY(Computers and Society)