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: "videocapsulenet a simplified network for action detection"
  Abstract: The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown extremely good results for video human action classification, however, action detection is still a challenging problem. The current action detection approaches follow a complex pipeline which involves multiple tasks such as tube proposals, optical flow, and tube classification. In this work, we present a more elegant solution for action detection based on the recently developed capsule network. We propose a 3D capsule network for videos, called VideoCapsuleNet: a unified network for action detection which can jointly perform pixel-wise action segmentation along with action classification. The proposed network is a generalization of capsule network from 2D to 3D, which takes a sequence of video frames as input. The 3D generalization drastically increases the number of capsules in the network, making capsule routing computationally expensive. We introduce capsule-pooling in the convolutional capsule layer to address this issue which makes the voting algorithm tractable. The routing-by-agreement in the network inherently models the action representations and various action characteristics are captured by the predicted capsules. This inspired us to utilize the capsules for action localization and the class-specific capsules predicted by the network are used to determine a pixel-wise localization of actions. The localization is further improved by parameterized skip connections with the convolutional capsule layers and the network is trained end-to-end with a classification as well as localization loss. The proposed network achieves sate-of-the-art performance on multiple action detection datasets including UCF-Sports, J-HMDB, and UCF-101 (24 classes) with an impressive ~20% improvement on UCF-101 and ~15% improvement on J-HMDB in terms of v-mAP scores.

Connected through hyperedge HE-1 (induced by source paper "examining the benefits of capsule neural networks"):
  with members:
    - "convolutional capsule network for classification of breast cancer histology images"
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "deep reinforcement learning using capsules in advanced game environments"
    - "compositional coding capsule network with k means routing for text classification"
    - "a capsule network for traffic speed prediction in complex road networks"

Connected through hyperedge HE-2 (induced by source paper "subspace capsule network"):
  with members:
    - "dual directed capsule network for very low resolution image recognition"
    - "investigating capsule networks with dynamic routing for text classification"
    - "cappronet deep feature learning via orthogonal projections onto capsule subspaces"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved techniques for training gans"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "you only watch once a unified cnn architecture for real time spatiotemporal action localization"):
  with members:
    - "resource efficient 3d convolutional neural networks"
    - "step spatio temporal progressive learning for video action detection"
    - "eco efficient convolutional network for online video understanding"
    - "predicting action tubes"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "dance with flow two in one stream action detection"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "modulating early visual processing by language"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-5 (induced by source paper "an efficient 3d cnn for action object segmentation in video"):
  with members:
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "actor and action video segmentation from a sentence"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learning to segment moving objects"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-6 (induced by source paper "mobile video action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "face focused cross stream network for deception detection in videos"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "compressed video action recognition"

Connected through hyperedge HE-7 (induced by source paper "deformable tube network for action detection in videos"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "slowfast networks for video recognition"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "focal loss for dense object detection"
    - "action tubelet detector for spatio temporal action localization"
    - "ava a video dataset of spatio temporally localized atomic visual actions"

Connected through hyperedge HE-8 (induced by source paper "capsulevos semi supervised video object segmentation using capsule routing"):
  with members:
    - "rvos end to end recurrent network for video object segmentation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "youtube vos sequence to sequence video object segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"

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)