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: "towards weakly supervised action localization"
  Abstract: This paper presents a novel approach for weakly-supervised action localization, i.e., that does not require per-frame spatial annotations for training. We first introduce an effective method for extracting human tubes by combining a state-of-the-art human detector with a tracking-by-detection approach. Our tube extraction leverages the large amount of annotated humans available today and outperforms the state of the art by an order of magnitude: with less than 5 tubes per video, we obtain a recall of 95% on the UCF-Sports and J-HMDB datasets. Given these human tubes, we perform weakly-supervised selection based on multi-fold Multiple Instance Learning (MIL) with improved dense trajectories and achieve excellent results. We obtain a mAP of 84% on UCF-Sports, 54% on J-HMDB and 45% on UCF-101, which outperforms the state of the art for weakly-supervised action localization and is close to the performance of the best fully-supervised approaches. #R##N#The second contribution of this paper is a new realistic dataset for action localization, named DALY (Daily Action Localization in YouTube). It contains high quality temporal and spatial annotations for 10 actions in 31 hours of videos (3.3M frames), which is an order of magnitude larger than standard action localization datasets. On the DALY dataset, our tubes have a spatial recall of 82%, but the detection task is extremely challenging, we obtain 10.8% mAP.

Connected through hyperedge HE-1 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph modules for modeling object object interactions in activity recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "simitate a hybrid imitation learning benchmark"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "one shot high fidelity imitation training large scale deep nets with rl"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "dart noise injection for robust imitation learning"
    - "human action localization with sparse spatial supervision"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-5 (induced by source paper "spatio temporal action recognition a survey"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "the kinetics human action video dataset"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "semi supervised classification with graph convolutional networks"
    - "learning to track for spatio temporal action localization"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal action localization in a weakly supervised setting"):
  with members:
    - "predicting action tubes"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "incremental tube construction for human action detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "object detection from video tubelets with convolutional neural networks"
    - "deep learning for detecting multiple space time action tubes in videos"
    - "automatic action annotation in weakly labeled videos"
    - "relaxed multiple instance svm with application to object discovery"

Connected through hyperedge HE-7 (induced by source paper "actor centric relation network"):
  with members:
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "slac a sparsely labeled dataset for action classification and localization"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "a simple neural network module for relational reasoning"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-8 (induced by source paper "learning to anonymize faces for privacy preserving action detection"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "learning latent super events to detect multiple activities in videos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)