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: "amtnet action micro tube regression by end to end trainable deep architecture"
  Abstract: Dominant approaches to action detection can only provide sub-optimal solutions to the problem, as they rely on seeking frame-level detections, to later compose them into "action tubes" in a post-processing step. With this paper we radically depart from current practice, and take a first step towards the design and implementation of a deep network architecture able to classify and regress whole video subsets, so providing a truly optimal solution of the action detection problem. In this work, in particular, we propose a novel deep net framework able to regress and classify 3D region proposals spanning two successive video frames, whose core is an evolution of classical region proposal networks (RPNs). As such, our 3D-RPN net is able to effectively encode the temporal aspect of actions by purely exploiting appearance, as opposed to methods which heavily rely on expensive flow maps. The proposed model is end-to-end trainable and can be jointly optimised for action localisation and classification in a single step. At test time the network predicts "micro-tubes" encompassing two successive frames, which are linked up into complete action tubes via a new algorithm which exploits the temporal encoding learned by the network and cuts computation time by 50%. Promising results on the J-HMDB-21 and UCF-101 action detection datasets show that our model does outperform the state-of-the-art when relying purely on appearance.

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 "stage spatio temporal attention on graph entities for video action detection"):
  with members:
    - "step spatio temporal progressive learning for video action detection"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "actor conditioned attention maps for video action detection"
    - "long term feature banks for detailed video understanding"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "video action transformer network"
    - "timeception for complex action recognition"

Connected through hyperedge HE-3 (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"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-4 (induced by source paper "pointly supervised action localization"):
  with members:
    - "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"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-5 (induced by source paper "progress regression rnn for online spatial temporal action localization in unconstrained videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "predictive corrective networks for action detection"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "action tubelet detector for spatio temporal action localization"
    - "temporal action localization by structured maximal sums"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "tracknet simultaneous object detection and tracking and its application in traffic video analysis"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "action tubelet detector for spatio temporal action localization"
    - "detect to track and track to detect"
    - "object detection in videos with tubelet proposal networks"
    - "mask r cnn"
    - "object detection from video tubelets with convolutional neural networks"
    - "yolo9000 better faster stronger"
    - "attentive contexts for object detection"

Connected through hyperedge HE-7 (induced by source paper "spatio temporal action localization in a weakly supervised setting"):
  with members:
    - "predicting action tubes"
    - "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"
    - "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-8 (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"
    - "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"
    - "temporal context network for activity localization in videos"

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)