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: "learning from video and text via large scale discriminative clustering"
  Abstract: Discriminative clustering has been successfully applied to a number of weakly-supervised learning tasks. Such applications include person and action recognition, text-to-video alignment, object co-segmentation and colocalization in videos and images. One drawback of discriminative clustering, however, is its limited scalability. We address this issue and propose an online optimization algorithm based on the Block-Coordinate Frank-Wolfe algorithm. We apply the proposed method to the problem of weakly supervised learning of actions and actors from movies together with corresponding movie scripts. The scaling up of the learning problem to 66 feature length movies enables us to significantly improve weakly supervised action recognition.

Connected through hyperedge HE-1 (induced by source paper "end to end learning of visual representations from uncurated instructional videos"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "learning video representations using contrastive bidirectional transformer"
    - "contrastive multiview coding"
    - "mining youtube a dataset for learning fine grained action concepts from webly supervised video data"
    - "a case study on combining asr and visual features for generating instructional video captions"
    - "toward self supervised object detection in unlabeled videos"
    - "unsupervised pre training of image features on non curated data"

Connected through hyperedge HE-2 (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-3 (induced by source paper "domain specific priors and meta learning for low shot first person action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "what have we learned from deep representations for action recognition"
    - "how to train your maml"
    - "towards an unequivocal representation of actions"
    - "probabilistic model agnostic meta learning"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-4 (induced by source paper "learning multimodal representations for unseen activities"):
  with members:
    - "fine grained activity recognition in baseball videos"
    - "joint event detection and description in continuous video streams"
    - "semantic autoencoder for zero shot learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "zero shot learning via class conditioned deep generative models"
    - "dense captioning events in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "m vad names a dataset for video captioning with naming"):
  with members:
    - "end to end face detection and cast grouping in movies using erd h o s r e nyi clustering"
    - "generating descriptions with grounded and co referenced people"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "moviegraphs towards understanding human centric situations from videos"
    - "dense captioning events in videos"
    - "hierarchical boundary aware neural encoder for video captioning"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "temporal localization of moments in video collections with natural language"):
  with members:
    - "do better imagenet models transfer better"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "diagnosing error in temporal action detectors"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "ctap complementary temporal action proposal generation"
    - "video object segmentation with language referring expressions"

Connected through hyperedge HE-7 (induced by source paper "deep discriminative clustering analysis"):
  with members:
    - "variational wasserstein clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep continuous clustering"
    - "relational inductive biases deep learning and graph networks"
    - "learning markov clustering networks for scene text detection"
    - "hierarchical clustering with structural constraints"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-8 (induced by source paper "self supervised learning of face representations for video face clustering"):
  with members:
    - "unsupervised learning of face representations"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generating descriptions with grounded and co referenced people"
    - "classification driven dynamic image enhancement"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "moviegraphs towards understanding human centric situations from videos"
    - "shuffle and learn unsupervised learning using temporal order verification"

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)