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: "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"
  Abstract: Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition. For the task of capturing temporal structure in video, however, there still remain numerous open research questions. Current research suggests using a simple temporal feature pooling strategy to take into account the temporal aspect of video. We demonstrate that this method is not sufficient for gesture recognition, where temporal information is more discriminative compared to general video classification tasks. We explore deep architectures for gesture recognition in video and propose a new end-to-end trainable neural network architecture incorporating temporal convolutions and bidirectional recurrence. Our main contributions are twofold; first, we show that recurrence is crucial for this task; second, we show that adding temporal convolutions leads to significant improvements. We evaluate the different approaches on the Montalbano gesture recognition dataset, where we achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "short term temporal convolutional networks for dynamic hand gesture recognition"):
  with members:
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "large scale isolated gesture recognition using convolutional neural networks"
    - "squeeze and excitation networks"
    - "rank pooling for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "improving action localization by progressive cross stream cooperation"):
  with members:
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "crowd counting using deep recurrent spatial aware network"
    - "modeling spatio temporal human track structure for action localization"
    - "actor centric relation network"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "action tubelet detector for spatio temporal action localization"
    - "tube convolutional neural network t cnn for action detection in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-4 (induced by source paper "recognizing american sign language manual signs from rgb d videos"):
  with members:
    - "video based sign language recognition without temporal segmentation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "the kinetics human action video dataset"
    - "rank pooling for action recognition"
    - "evaluation of deep learning based pose estimation for sign language recognition"

Connected through hyperedge HE-5 (induced by source paper "dynamic gesture recognition by using cnns and star rgb a temporal information condensation"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "convolutional two stream network fusion for video action recognition"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "progression modelling for online and early gesture detection"):
  with members:
    - "encouraging lstms to anticipate actions very early"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "efficient object localization using convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "moddrop adaptive multi modal gesture recognition"

Connected through hyperedge HE-7 (induced by source paper "detecting motorcycle helmet use with deep learning"):
  with members:
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "can composite appearance network and a novel evaluation metric for person tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "memory based online learning of deep representations from video streams"
    - "multi camera multi object tracking"
    - "neural aggregation network for video face recognition"
    - "posetrack joint multi person pose estimation and tracking"

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)