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: "moments in time dataset one million videos for event understanding"
  Abstract: We present the Moments in Time Dataset, a large-scale human-annotated collection of one million short videos corresponding to dynamic events unfolding within three seconds. Modeling the spatial-audio-temporal dynamics even for actions occurring in 3 second videos poses many challenges: meaningful events do not include only people, but also objects, animals, and natural phenomena; visual and auditory events can be symmetrical or not in time ("opening" means "closing" in reverse order), and transient or sustained. We describe the annotation process of our dataset (each video is tagged with one action or activity label among 339 different classes), analyze its scale and diversity in comparison to other large-scale video datasets for action recognition, and report results of several baseline models addressing separately and jointly three modalities: spatial, temporal and auditory. The Moments in Time dataset designed to have a large coverage and diversity of events in both visual and auditory modalities, can serve as a new challenge to develop models that scale to the level of complexity and abstract reasoning that a human processes on a daily basis.

Connected through hyperedge HE-1 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta learning"
    - "autoaugment learning augmentation policies from data"
    - "slowfast networks for video recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-2 (induced by source paper "constraining temporal relationship for action localization"):
  with members:
    - "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"
    - "graph convolutional networks for temporal action localization"
    - "cornernet detecting objects as paired keypoints"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "soft nms improving object detection with one line of code"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "how to train your maml"

Connected through hyperedge HE-4 (induced by source paper "relational action forecasting"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "recurrent relational networks for complex relational reasoning"
    - "relational recurrent neural networks"
    - "actor centric relation network"
    - "relational inductive biases deep learning and graph networks"
    - "object level visual reasoning in videos"
    - "predicting deeper into the future of semantic segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (induced by source paper "videograph recognizing minutes long human activities in videos"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "scaling egocentric vision the epic kitchens dataset"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "video time properties encoders and evaluation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "video action transformer network"
    - "timeception for complex action recognition"

Connected through hyperedge HE-6 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "this looks like that deep learning for interpretable image recognition"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-7 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "a short note about kinetics 600"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "appearance and relation networks for video classification"
    - "interpretable 3d human action analysis with temporal convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "videobert a joint model for video and language representation learning"):
  with members:
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual language model pretraining"
    - "coin a large scale dataset for comprehensive instructional video analysis"
    - "neural baby talk"
    - "stochastic adversarial video prediction"
    - "deep contextualized word representations"
    - "end to end dense video captioning with masked transformer"
    - "stochastic video generation with a learned prior"

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)