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: "sequential deep trajectory descriptor for action recognition with three stream cnn"
  Abstract: Learning the spatial-temporal representation of motion information is crucial to human action recognition. Nevertheless, most of the existing features or descriptors cannot capture motion information effectively, especially for long-term motion. To address this problem, this paper proposes a long-term motion descriptor called sequential deep trajectory descriptor (sDTD). Specifically, we project dense trajectories into two-dimensional planes, and subsequently a CNN-RNN network is employed to learn an effective representation for long-term motion. Unlike the popular two-stream ConvNets, the sDTD stream is introduced into a three-stream framework so as to identify actions from a video sequence. Consequently, this three-stream framework can simultaneously capture static spatial features, short-term motion, and long-term motion in the video. Extensive experiments were conducted on three challenging datasets: KTH, HMDB51, and UCF101. Experimental results show that our method achieves state-of-the-art performance on the KTH and UCF101 datasets, and is comparable to the state-of-the-art methods on the HMDB51 dataset.

Connected through hyperedge HE-1 (induced by source paper "kernel quantization for efficient network compression"):
  with members:
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "video inpainting by jointly learning temporal structure and spatial details"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "towards the limit of network quantization"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-3 (induced by source paper "temporal action localization using long short term dependency"):
  with members:
    - "weakly supervised visual instrument playing action detection in videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "compressed video action recognition"
    - "single shot temporal action detection"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-4 (induced by source paper "deep multi kernel convolutional lstm networks and an attention based mechanism for videos"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "future semantic segmentation with convolutional lstm"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deeprain convlstm network for precipitation prediction using multichannel radar data"
    - "the kinetics human action video dataset"
    - "diversified visual attention networks for fine grained object classification"
    - "the pose knows video forecasting by generating pose futures"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "interaction relational network for mutual action recognition"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "hierarchical long short term concurrent memory for human interaction recognition"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation with relational networks"
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-6 (induced by source paper "probabilistic semantic retrieval for surveillance videos with activity graphs"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "simple online and realtime tracking"
    - "fast zero shot image tagging"
    - "person re identification by discriminative selection in video ranking"
    - "evaluating two stream cnn for video classification"
    - "deep residual learning for image recognition"
    - "zero shot learning via semantic similarity embedding"

Connected through hyperedge HE-7 (induced by source paper "p odn prototype based open deep network for open set recognition"):
  with members:
    - "robust classification with convolutional prototype learning"
    - "generative openmax for multi class open set classification"
    - "learning long term dependencies for action recognition with a biologically inspired deep network"
    - "temporal segment networks towards good practices for deep action recognition"
    - "tensorflow a system for large scale machine learning"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-8 (induced by source paper "temporal spatial mapping for action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "spatiotemporal pyramid network for video action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video 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)