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: "video highlight prediction using audience chat reactions"
  Abstract: Sports channel video portals offer an exciting domain for research on multimodal, multilingual analysis. We present methods addressing the problem of automatic video highlight prediction based on joint visual features and textual analysis of the real-world audience discourse with complex slang, in both English and traditional Chinese. We present a novel dataset based on League of Legends championships recorded from North American and Taiwanese Twitch.tv channels (will be released for further research), and demonstrate strong results on these using multimodal, character-level CNN-RNN model architectures.

Connected through hyperedge HE-1 (induced by source paper "e sports talent scouting based on multimodal twitch stream data"):
  with members:
    - "twitch plays pokemon machine learns twitch unsupervised context aware anomaly detection for identifying trolls in streaming data"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "in defense of the triplet loss for person re identification"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "soundnet learning sound representations from unlabeled video"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "gendered conversation in a social game streaming platform"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "towards extracting highlights from recorded live videos an implicit crowdsourcing approach"):
  with members:
    - "deeplens towards a visual data management system"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "real time video highlights for yahoo esports"
    - "video summarization using deep semantic features"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "your click decides your fate leveraging clickstream patterns in mooc videos to infer students information processing and attrition behavior"
    - "generating sequences with recurrent neural networks"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-3 (induced by source paper "unsupervised multi stream highlight detection for the game honor of kings"):
  with members:
    - "less is more learning highlight detection from video duration"
    - "deep unsupervised multi view detection of video game stream highlights"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "video2gif automatic generation of animated gifs from video"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "game based video context dialogue"):
  with members:
    - "chalet cornell house agent learning environment"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "a structured self attentive sentence embedding"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "a persona based neural conversation model"
    - "multiresolution recurrent neural networks an application to dialogue response generation"

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)