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: "youtube av 50k an annotated corpus for comments in autonomous vehicles"
  Abstract: Ahstract- With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.

Connected through hyperedge HE-1 (induced by source paper "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"):
  with members:
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "a methodology for studying 802 11p vanet broadcasting performance with practical vehicle distribution"

Connected through hyperedge HE-2 (induced by source paper "semi supervised text regression with conditional generative adversarial networks"):
  with members:
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "attributes in multiple facial images"
    - "adversarial learning for neural dialogue generation"
    - "adversarial ranking for language generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "music sequence prediction with mixture hidden markov models"):
  with members:
    - "understanding beauty via deep facial features"
    - "anonymousnet natural face de identification with measurable privacy"
    - "component attention guided face super resolution network cagface"
    - "multiclass information flow propagation control under vehicle to vehicle communication environments"
    - "cooperative adaptive cruise control for connected autonomous vehicles by factoring communication related constraints"
    - "cooperative adaptive cruise control for a platoon of connected and autonomous vehicles considering dynamic information flow topology"
    - "towards reading hidden emotions a comparative study of spontaneous micro expression spotting and recognition methods"
    - "the similarity metric"

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.CL(Computation and Language)