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: "localizing actions from video labels and pseudo annotations"
  Abstract: The goal of this paper is to determine the spatio-temporal location of actions in video. Where training from hard to obtain box annotations is the norm, we propose an intuitive and effective algorithm that localizes actions from their class label only. We are inspired by recent work showing that unsupervised action proposals selected with human point-supervision perform as well as using expensive box annotations. Rather than asking users to provide point supervision, we propose fully automatic visual cues that replace manual point annotations. We call the cues pseudo-annotations, introduce five of them, and propose a correlation metric for automatically selecting and combining them. Thorough evaluation on challenging action localization datasets shows that we reach results comparable to results with full box supervision. We also show that pseudo-annotations can be leveraged during testing to improve weakly- and strongly-supervised localizers.

Connected through hyperedge HE-1 (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"
    - "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"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-2 (induced by source paper "tacnet transition aware context network for spatio temporal action detection"):
  with members:
    - "temporal action detection with structured segment networks"
    - "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"
    - "deep learning for detecting multiple space time action tubes in videos"

Connected through hyperedge HE-3 (induced by source paper "guess where actor supervision for spatiotemporal action localization"):
  with members:
    - "videocapsulenet a simplified network for action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "actor centric relation network"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "action tubelet detector for spatio temporal action localization"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal instance learning action tubes from class supervision"):
  with members:
    - "pointly supervised action localization"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "human action localization with sparse spatial supervision"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "incremental tube construction for human action detection"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-5 (induced by source paper "a flexible model for training action localization with varying levels of supervision"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "human action localization with sparse spatial supervision"
    - "the kinetics human action video dataset"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-6 (induced by source paper "actor and action video segmentation from a sentence"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "person search with natural language description"
    - "temporal action detection with structured segment networks"
    - "the kinetics human action video dataset"
    - "tall temporal activity localization via language query"
    - "spatio temporal person retrieval via natural language queries"
    - "spatial aware object embeddings for zero shot localization and classification of actions"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-7 (induced by source paper "spatial aware object embeddings for zero shot localization and classification of actions"):
  with members:
    - "video stream retrieval of unseen queries using semantic memory"
    - "spot on action localization from pointly supervised proposals"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "multi task zero shot action recognition with prioritised data augmentation"
    - "yfcc100m the new data in multimedia research"
    - "learning attributes equals multi source domain generalization"
    - "videostory embeddings recognize events when examples are scarce"
    - "semantic embedding space for zero shot action recognition"

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)