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: "real time video highlights for yahoo esports"
  Abstract: Esports has gained global popularity in recent years and several companies have started offering live streaming videos of esports games and events. This creates opportunities to develop large scale video understanding systems for new product features and services. We present a technique for detecting highlights from live streaming videos of esports game matches. Most video games use pronounced visual effects to emphasize highlight moments; we use CNNs to learn convolution filters of those visual effects for detecting highlights. We propose a cascaded prediction approach that allows us to deal with several challenges arise in a production environment. We demonstrate our technique on our new dataset of three popular game titles, Heroes of the Storm, League of Legends, and Dota 2. Our technique achieves 18 FPS on a single CPU with an average precision of up to 83.18%. Part of our technique is currently deployed in production on Yahoo Esports.

Connected through hyperedge HE-1 (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 highlight prediction using audience chat reactions"
    - "video highlights detection and summarization with lag calibration based on concept emotion mapping of crowd sourced time sync comments"
    - "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-2 (induced by source paper "rethinking the evaluation of video summaries"):
  with members:
    - "query adaptive video summarization via quality aware relevance estimation"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "query focused extractive video summarization"
    - "video summarization using deep semantic features"
    - "videoset video summary evaluation through text"

Connected through hyperedge HE-3 (induced by source paper "video highlight prediction using audience chat reactions"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "hierarchical question image co attention for visual question answering"
    - "dynamic memory networks for visual and textual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "vqa visual question answering"

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)