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: "hacs human action clips and segments dataset for recognition and temporal localization"
  Abstract: This paper presents a new large-scale dataset for recognition and temporal localization of human actions collected from Web videos. We refer to it as HACS (Human Action Clips and Segments). We leverage both consensus and disagreement among visual classifiers to automatically mine candidate short clips from unlabeled videos, which are subsequently validated by human annotators. The resulting dataset is dubbed HACS Clips. Through a separate process we also collect annotations defining action segment boundaries. This resulting dataset is called HACS Segments. Overall, HACS Clips consists of 1.5M annotated clips sampled from 504K untrimmed videos, and HACS Seg-ments contains 139K action segments densely annotatedin 50K untrimmed videos spanning 200 action categories. HACS Clips contains more labeled examples than any existing video benchmark. This renders our dataset both a large scale action recognition benchmark and an excellent source for spatiotemporal feature learning. In our transferlearning experiments on three target datasets, HACS Clips outperforms Kinetics-600, Moments-In-Time and Sports1Mas a pretraining source. On HACS Segments, we evaluate state-of-the-art methods of action proposal generation and action localization, and highlight the new challenges posed by our dense temporal annotations.

Connected through hyperedge HE-1 (induced by source paper "sf net single frame supervision for temporal action localization"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "3c net category count and center loss for weakly supervised action localization"
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "weakly supervised action localization with background modeling"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"

Connected through hyperedge HE-2 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

Connected through hyperedge HE-3 (induced by source paper "refineloc iterative refinement for weakly supervised action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "unsupervised pre training of image features on non curated data"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "weakly supervised action localization with background modeling"
    - "bmn boundary matching network for temporal action proposal generation"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-4 (induced by source paper "distinit learning video representations without a single labeled video"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "taskonomy disentangling task transfer learning"
    - "a better baseline for ava"
    - "video action transformer network"
    - "unsupervised representation learning by predicting image rotations"
    - "spatio temporal channel correlation networks for action classification"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (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"
    - "moments in time dataset one million videos for event understanding"
    - "neural baby talk"
    - "stochastic adversarial video prediction"
    - "deep contextualized word representations"
    - "end to end dense video captioning with masked transformer"

Connected through hyperedge HE-6 (induced by source paper "large scale holistic video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "dynamonet dynamic action and motion network"
    - "distinit learning video representations without a single labeled video"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"

Connected through hyperedge HE-7 (induced by source paper "attentive action and context factorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "non local neural networks"
    - "attention is all you need"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-8 (induced by source paper "holistic large scale video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "dynamonet dynamic action and motion network"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

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)