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: "tennisvid2text fine grained descriptions for domain specific videos"
  Abstract: Automatically describing videos has ever been fascinating. In this work, we attempt to describe videos from a specific domain - broadcast videos of lawn tennis matches. Given a video shot from a tennis match, we intend to generate a textual commentary similar to what a human expert would write on a sports website. Unlike many recent works that focus on generating short captions, we are interested in generating semantically richer descriptions. This demands a detailed low-level analysis of the video content, specially the actions and interactions among subjects. We address this by limiting our domain to the game of lawn tennis. Rich descriptions are generated by leveraging a large corpus of human created descriptions harvested from Internet. We evaluate our method on a newly created tennis video data set. Extensive analysis demonstrate that our approach addresses both semantic correctness as well as readability aspects involved in the task.

Connected through hyperedge HE-1 (induced by source paper "crowd video captioning"):
  with members:
    - "emotion based crowd representation for abnormality detection"
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "translating videos to natural language using deep recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-2 (induced by source paper "smarttennistv automatic indexing of tennis videos"):
  with members:
    - "synthetic data for text localisation in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "synthetic data and artificial neural networks for natural scene text recognition"

Connected through hyperedge HE-3 (induced by source paper "towards structured analysis of broadcast badminton videos"):
  with members:
    - "am i a baller basketball performance assessment from first person videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "wavenet a generative model for raw audio"
    - "temporal convolutional networks a unified approach to action segmentation"
    - "learning to track at 100 fps with deep regression networks"
    - "trajectory aligned features for first person action recognition"
    - "deep residual learning for image recognition"
    - "a hierarchical deep temporal model for group activity recognition"

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.CV(Computer Vision and Pattern Recognition)