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: "soccernet a scalable dataset for action spotting in soccer videos"
  Abstract: In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances d ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.

Connected through hyperedge HE-1 (induced by source paper "a hybrid approach for tracking individual players in broadcast match videos"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "fully convolutional siamese networks for object tracking"
    - "high speed tracking with kernelized correlation filters"

Connected through hyperedge HE-2 (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-3 (induced by source paper "golfdb a video database for golf swing sequencing"):
  with members:
    - "rethinking imagenet pre training"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a better baseline for ava"
    - "real time end to end action detection with two stream networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-4 (induced by source paper "comprehensive soccer video understanding towards human comparable video understanding system in constrained environment"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "bmn boundary matching network for temporal action proposal generation"
    - "videos as space time region graphs"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "youtube 8m a large scale video classification benchmark"
    - "detecting events and key actors in multi person videos"

Connected through hyperedge HE-5 (induced by source paper "learning sports camera selection from internet videos"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "pano2vid automatic cinematography for watching 360 circ videos"
    - "r fcn object detection via region based fully convolutional networks"
    - "a hierarchical deep temporal model for group activity recognition"
    - "detecting events and key actors in multi person videos"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "matrix completion from a few entries"

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)