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: "human action localization with sparse spatial supervision"
  Abstract: We introduce an approach for spatio-temporal human action localization using sparse spatial supervision. Our method leverages the large amount of annotated humans available today and extracts human tubes by combining a state-of-the-art human detector with a tracking-by-detection approach. Given these high-quality human tubes and temporal supervision, we select positive and negative tubes with very sparse spatial supervision, i.e., only one spatially annotated frame per instance. The selected tubes allow us to effectively learn a spatio-temporal action detector based on dense trajectories or CNNs. We conduct experiments on existing action localization benchmarks: UCF-Sports, J-HMDB and UCF-101. Our results show that our approach, despite using sparse spatial supervision, performs on par with methods using full supervision, i.e., one bounding box annotation per frame. #R##N#To further validate our method, we introduce DALY (Daily Action Localization in YouTube), a dataset for realistic action localization in space and time. It contains high quality temporal and spatial annotations for 3.6k instances of 10 actions in 31 hours of videos (3.3M frames). It is an order of magnitude larger than existing datasets, with more diversity in appearance and long untrimmed videos.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "one shot imitation learning"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal instance learning action tubes from class supervision"):
  with members:
    - "pointly supervised action localization"
    - "localizing actions from video labels and pseudo annotations"
    - "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"
    - "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-4 (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"
    - "localizing actions from video labels and pseudo annotations"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "the kinetics human action video dataset"
    - "learning from video and text via large scale discriminative clustering"

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)