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: "hierarchical attention network for action recognition in videos"
  Abstract: Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i.e. it explicitly models the temporal transitions between frames as well as video segments and (3) with a multiple step spatial temporal attention mechanism, HAN automatically learns important regions in video frames and temporal segments in the video. The proposed model is trained and evaluated on the standard video action benchmarks, i.e., UCF-101 and HMDB-51, and it significantly outperforms the state-of-the arts

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for assessing physical rehabilitation exercises"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "temporal action detection with structured segment networks"
    - "deep representation learning for human motion prediction and classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"

Connected through hyperedge HE-2 (induced by source paper "eleatt rnn adding attentiveness to neurons in recurrent neural networks"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "cbam convolutional block attention module"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "david dual attentional video deblurring"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "learning to optimize in swarms"
    - "deblurgan v2 deblurring orders of magnitude faster and better"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"

Connected through hyperedge HE-4 (induced by source paper "sr lstm state refinement for lstm towards pedestrian trajectory prediction"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "videos as space time region graphs"
    - "vip cnn visual phrase guided convolutional neural network"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised completion moment detection using temporal attention"):
  with members:
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "temporal cycle consistency learning"
    - "action completion a temporal model for moment detection"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "r c3d region convolutional 3d network for temporal activity detection"

Connected through hyperedge HE-6 (induced by source paper "interpretable spatio temporal attention for video action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "human action recognition and prediction a survey"
    - "moments in time dataset one million videos for event understanding"
    - "learn to pay attention"
    - "tell me where to look guided attention inference network"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"

Connected through hyperedge HE-7 (induced by source paper "deep adaptive temporal pooling for activity recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "efficient and deep person re identification using multi level similarity"
    - "non linear temporal subspace representations for activity recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-8 (induced by source paper "skeleton based human action recognition with global context aware attention lstm networks"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "online human action detection using joint classification regression recurrent neural networks"

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)