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: "action recognition by hierarchical mid level action elements"
  Abstract: Realistic videos of human actions exhibit rich spatiotemporal structures at multiple levels of granularity: an action can always be decomposed into multiple finer-grained elements in both space and time. To capture this intuition, we propose to represent videos by a hierarchy of mid-level action elements (MAEs), where each MAE corresponds to an action-related spatiotemporal segment in the video. We introduce an unsupervised method to generate this representation from videos. Our method is capable of distinguishing action-related segments from background segments and representing actions at multiple spatiotemporal resolutions. Given a set of spatiotemporal segments generated from the training data, we introduce a discriminative clustering algorithm that automatically discovers MAEs at multiple levels of granularity. We develop structured models that capture a rich set of spatial, temporal and hierarchical relations among the segments, where the action label and multiple levels of MAE labels are jointly inferred. The proposed model achieves state-of-the-art performance in multiple action recognition benchmarks. Moreover, we demonstrate the effectiveness of our model in real-world applications such as action recognition in large-scale untrimmed videos and action parsing.

Connected through hyperedge HE-1 (induced by source paper "a novel online action detection framework from untrimmed video streams"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "temporal action detection with structured segment networks"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "unsupervised learning for physical interaction through video prediction"
    - "online action detection"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-2 (induced by source paper "context aware query selection for active learning in event recognition"):
  with members:
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "temporal convolutional networks for action segmentation and detection"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "hierarchical subquery evaluation for active learning on a graph"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "temporal cycle consistency learning"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "action completion a temporal model for moment detection"
    - "playing hard exploration games by watching youtube"
    - "unsupervised learning and segmentation of complex activities from video"
    - "neighbourhood consensus networks"
    - "deep unsupervised learning of visual similarities"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "second order temporal pooling for action recognition"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "non linear temporal subspace representations for activity recognition"
    - "video representation learning using discriminative pooling"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generalized rank pooling for activity recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "hierarchical boundary aware neural encoder for video captioning"):
  with members:
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "temporal segment networks towards good practices for deep action recognition"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "hierarchical multiscale recurrent neural networks"
    - "jointly modeling embedding and translation to bridge video and language"
    - "the long short story of movie description"
    - "a deep siamese network for scene detection in broadcast videos"
    - "a dataset for movie description"

Connected through hyperedge HE-6 (induced by source paper "discriminatively learned hierarchical rank pooling networks"):
  with members:
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "action recognition with dynamic image networks"
    - "youtube 8m a large scale video classification benchmark"
    - "convolutional two stream network fusion for video action recognition"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "exploiting image trained cnn architectures for unconstrained video classification"

Connected through hyperedge HE-7 (induced by source paper "unsupervised video understanding by reconciliation of posture similarities"):
  with members:
    - "multi context attention for human pose estimation"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "convolutional two stream network fusion for video action recognition"
    - "generating videos with scene dynamics"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "anticipating visual representations from unlabeled video"

Connected through hyperedge HE-8 (induced by source paper "learning social affordance grammar from videos transferring human interactions to human robot interactions"):
  with members:
    - "learning social affordance for human robot interaction"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "joint inference of groups events and human roles in aerial 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)