human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a survey on resource allocation in vehicular networks"):
  Abstract: Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory quality of service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as hostile wireless channels (e.g., high mobility or asynchronous transmissions), increasingly fragmented and congested spectrum, hardware imperfections, and explosive growth of vehicular communication devices. Therefore, it is highly desirable to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation (RA) schemes for a range of vehicular network technologies including dedicated short range communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

Includes members:
  Member "toward intelligent vehicular networks a machine learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "radio resource allocation for reliable out of coverage v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vrls a unified reinforcement learning scheduler for vehicle to vehicle communications")
  Member "deep reinforcement learning for resource allocation in v2v communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications")
  Member "deep reinforcement learning a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "vehicular communications a network layer perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a virtual network customization framework for multicast services in nfv enabled core networks")
  Member "an end to end network slicing framework for 5g wireless communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "reinforcement learning for resource provisioning in the vehicular cloud":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges")
  Member "coding based data broadcasting for time critical applications with rate adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "instantly decodable network coding for real time scalable video broadcast over wireless networks")

Question: Please tell me which class the source paper 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.IT(Information Theory)