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: "spot on action localization from pointly supervised proposals"
  Abstract: We strive for spatio-temporal localization of actions in videos. The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations. Annotating action boxes in video is cumbersome, tedious, and error prone. Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only. We introduce an overlap measure between action proposals and points and incorporate them all into the objective of a non-convex Multiple Instance Learning optimization. Experimental evaluation on the UCF Sports and UCF 101 datasets shows that (i) spatio-temporal proposals can be used to train classifiers while retaining the localization performance, (ii) point annotations yield results comparable to box annotations while being significantly faster to annotate, (iii) with a minimum amount of supervision our approach is competitive to the state-of-the-art. Finally, we introduce spatio-temporal action annotations on the train and test videos of Hollywood2, resulting in Hollywood2Tubes, available at this http URL

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 "relational action forecasting"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "lpat learning to predict adaptive threshold for weakly supervised temporal action localization"):
  with members:
    - "dmc net generating discriminative motion cues for fast compressed video action recognition"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

Connected through hyperedge HE-4 (induced by source paper "pointly supervised action localization"):
  with members:
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "extreme clicking for efficient object annotation"
    - "localizing actions from video labels and pseudo annotations"
    - "action tubelet detector for spatio temporal action localization"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "pathtrack fast trajectory annotation with path supervision"

Connected through hyperedge HE-5 (induced by source paper "action recognition from single timestamp supervision in untrimmed videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "a flexible model for training action localization with varying levels of supervision"
    - "w talc weakly supervised temporal activity localization and classification"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-6 (induced by source paper "3c net category count and center loss for weakly supervised action localization"):
  with members:
    - "object counting and instance segmentation with image level supervision"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "c wsl count guided weakly supervised localization"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-7 (induced by source paper "efficient object annotation via speaking and pointing"):
  with members:
    - "natural vocabulary emerges from free form annotations"
    - "scaling egocentric vision the epic kitchens dataset"
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "object referring in visual scene with spoken language"

Connected through hyperedge HE-8 (induced by source paper "tacnet transition aware context network for spatio temporal action detection"):
  with members:
    - "temporal action detection with structured segment networks"
    - "localizing actions from video labels and pseudo annotations"
    - "dssd deconvolutional single shot detector"
    - "action tubelet detector for spatio temporal action localization"
    - "am i done predicting action progress in videos"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "ssd single shot multibox detector"

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)