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: "machine learning for vehicular networks"
  Abstract: The emerging vehicular networks are expected to make everyday vehicular operation safer, greener, and more efficient, and pave the path to autonomous driving in the advent of the fifth generation (5G) cellular system. Machine learning, as a major branch of artificial intelligence, has been recently applied to wireless networks to provide a data-driven approach to solve traditionally challenging problems. In this article, we review recent advances in applying machine learning in vehicular networks and attempt to bring more attention to this emerging area. After a brief overview of the major concept of machine learning, we present some application examples of machine learning in solving problems arising in vehicular networks. We finally discuss and highlight several open issues that warrant further research.

Connected through hyperedge HE-1 (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-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"
    - "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"
    - "millimeter wave vehicular communication to support massive automotive sensing"

Connected through hyperedge HE-3 (induced by source paper "test an end to end network traffic examination and identification framework based on spatio temporal features extraction"):
  with members:
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-4 (induced by source paper "deep learning aided application scheduler for vehicular safety communication"):
  with members:
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning based resource allocation for v2v communications"):
  with members:
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "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"
    - "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-6 (induced by source paper "vehicular communications a network layer perspective"):
  with members:
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "millimeter wave vehicular communication to support massive automotive sensing"
    - "multihop cluster based ieee 802 11p and lte hybrid architecture for vanet safety message dissemination"

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.IT(Information Theory)