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: "leveraging contextual cues for generating basketball highlights"
  Abstract: The massive growth of sports videos has resulted in a need for automatic generation of sports highlights that are comparable in quality to the hand-edited highlights produced by broadcasters such as ESPN. Unlike previous works that mostly use audio-visual cues derived from the video, we propose an approach that additionally leverages contextual cues derived from the environment that the game is being played in. The contextual cues provide information about the excitement levels in the game, which can be ranked and selected to automatically produce high-quality basketball highlights. We introduce a new dataset of 25 NCAA games along with their play-by-play stats and the ground-truth excitement data for each basket. We explore the informativeness of five different cues derived from the video and from the environment through user studies. Our experiments show that for our study participants, the highlights produced by our system are comparable to the ones produced by ESPN for the same games.

Connected through hyperedge HE-1 (induced by source paper "a context aware loss function for action spotting in soccer videos"):
  with members:
    - "golfdb a video database for golf swing sequencing"
    - "gaussian temporal awareness networks for action localization"
    - "pan tilt zoom slam for sports videos"
    - "bmn boundary matching network for temporal action proposal generation"
    - "fine grained activity recognition in baseball videos"
    - "diagnosing error in temporal action detectors"
    - "temporal hockey action recognition via pose and optical flows"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "neural re simulation for generating bounces in single images"):
  with members:
    - "bounce and learn modeling scene dynamics with real world bounces"
    - "neural rerendering in the wild"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "ignor image guided neural object rendering"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "learning to represent mechanics via long term extrapolation and interpolation"

Connected through hyperedge HE-3 (induced by source paper "bounce and learn modeling scene dynamics with real world bounces"):
  with members:
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "learning to represent mechanics via long term extrapolation and interpolation"
    - "learning to fly by crashing"
    - "forecasting human dynamics from static images"
    - "visual semantic planning using deep successor representations"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "marr revisited 2d 3d alignment via surface normal prediction"

Connected through hyperedge HE-4 (induced by source paper "soccernet a scalable dataset for action spotting in soccer videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "temporal action detection with structured segment networks"
    - "learnable pooling with context gating for video classification"
    - "temporal context network for activity localization in videos"
    - "action tubelet detector for spatio temporal action localization"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-5 (induced by source paper "automatic curation of golf highlights using multimodal excitement features"):
  with members:
    - "ssd single shot multibox detector"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "video summarization with long short term memory"
    - "context encoders feature learning by inpainting"
    - "soundnet learning sound representations from unlabeled video"
    - "colorful image colorization"
    - "learning image representations tied to ego motion"

Connected through hyperedge HE-6 (induced by source paper "a parameterized approach to personalized variable length summarization of soccer matches"):
  with members:
    - "what players do with the ball a physically constrained interaction modeling"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "elasticplay interactive video summarization with dynamic time budgets"):
  with members:
    - "video2gif automatic generation of animated gifs from video"
    - "cinemagazer a system for watching video at very high speed"

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.MM(Multimedia)