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: "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
  Abstract: We propose a novel method for temporally pooling frames in a video for the task of human action recognition. The method is motivated by the observation that there are only a small number of frames which, together, contain sufficient information to discriminate an action class present in a video, from the rest. The proposed method learns to pool such discriminative and informative frames, while discarding a majority of the non-informative frames in a single temporal scan of the video. Our algorithm does so by continuously predicting the discriminative importance of each video frame and subsequently pooling them in a deep learning framework. We show the effectiveness of our proposed pooling method on standard benchmarks where it consistently improves on baseline pooling methods, with both RGB and optical flow based Convolutional networks. Further, in combination with complementary video representations, we show results that are competitive with respect to the state-of-the-art results on two challenging and publicly available benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "an information rich sampling technique over spatio temporal cnn for classification of human actions in videos"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "lattice long short term memory for human action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "adam a method for stochastic optimization"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-2 (induced by source paper "dnanet de normalized attention based multi resolution network for human pose estimation"):
  with members:
    - "convolutional spatial attention model for reading comprehension with multiple choice questions"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "deep high resolution representation learning for human pose estimation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "pose flow efficient online pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "look into person joint body parsing pose estimation network and a new benchmark"

Connected through hyperedge HE-3 (induced by source paper "a comparative review of recent kinect based action recognition algorithms"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "spatio temporal graph convolution for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "discriminative video representation learning using support vector classifiers"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "a short note about kinetics 600"
    - "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"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "object detection in video with spatial temporal context aggregation"):
  with members:
    - "universal transformers"
    - "object detection in video with spatiotemporal sampling networks"
    - "optimizing video object detection via a scale time lattice"
    - "object detection in videos by high quality object linking"
    - "object detection in videos by short and long range object linking"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "non local neural networks"
    - "flow guided feature aggregation for video object detection"

Connected through hyperedge HE-6 (induced by source paper "micro expression action unit detection withspatio temporal adaptive pooling"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "deep extreme multi label learning"
    - "optimizing filter size in convolutional neural networks for facial action unit recognition"
    - "contextual action recognition with r cnn"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-7 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "learning where to attend like a human driver"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"

Connected through hyperedge HE-8 (induced by source paper "interaction aware spatio temporal pyramid attention networks for action classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attend and interact higher order object interactions for video understanding"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "attentional pooling for action recognition"

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)