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: "incremental tube construction for human action detection"
  Abstract: Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current systems fall short, either because they only detect one action per video, or because they assume that the entire video is available ahead of time. In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the detection-window association and action labelling problems jointly in a single pass. We demonstrate superior online association accuracy and speed (2.2ms per frame) as compared to the current state-of-the-art offline systems. We further demonstrate that the entire action detection pipeline can easily be made to work effectively in real-time using our action tube construction algorithm.

Connected through hyperedge HE-1 (induced by source paper "why can t i dance in the mall learning to mitigate scene bias in action recognition"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "bias resilient neural network"
    - "long term feature banks for detailed video understanding"
    - "women also snowboard overcoming bias in captioning models"
    - "algorithms and theory for multiple source adaptation"
    - "dropblock a regularization method for convolutional networks"
    - "learning not to learn training deep neural networks with biased data"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-2 (induced by source paper "dance with flow two in one stream action detection"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "videocapsulenet a simplified network for action detection"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "modulating early visual processing by language"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards weakly supervised action localization"
    - "automatic action annotation in weakly labeled videos"
    - "relaxed multiple instance svm with application to object discovery"

Connected through hyperedge HE-4 (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"
    - "human action localization with sparse spatial supervision"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-5 (induced by source paper "predicting action tubes"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "action tubelet detector for spatio temporal action localization"
    - "spatio temporal action detection with cascade proposal and location anticipation"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "encouraging lstms to anticipate actions very early"

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)