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: "deep quantization encoding convolutional activations with deep generative model"
  Abstract: Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative model, by training them in an end-to-end manner. To incorporate FV encoding strategy into deep generative models, we introduce Variational Auto-Encoder model, which steers a variational inference and learning in a neural network which can be straightforwardly optimized using standard stochastic gradient method. Different from the FV characterized by conventional generative models (e.g., Gaussian Mixture Model) which parsimoniously fit a discrete mixture model to data distribution, the proposed FV-VAE is more flexible to represent the natural property of data for better generalization. Extensive experiments are conducted on three public datasets, i.e., UCF101, ActivityNet, and CUB-200-2011 in the context of video action recognition and fine-grained image classification, respectively. Superior results are reported when compared to state-of-the-art representations. Most remarkably, our proposed FV-VAE achieves to-date the best published accuracy of 94.2% on UCF101.

Connected through hyperedge HE-1 (induced by source paper "convolutional neural network pruning using filter attenuation"):
  with members:
    - "modeling of pruning techniques for deep neural networks simplification"
    - "approximated oracle filter pruning for destructive cnn width optimization"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "prune the convolutional neural networks with sparse shrink"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "customizable architecture search for semantic segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "darts differentiable architecture search"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "learning spatio temporal representation with local and global diffusion"):
  with members:
    - "yh technologies at activitynet challenge 2018"
    - "exploiting spatial temporal modelling and multi modal fusion for human action recognition"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "non local neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-4 (induced by source paper "decoupling localization and classification in single shot temporal action detection"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "learning spatio temporal representation with local and global diffusion"
    - "jointly localizing and describing events for dense video captioning"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "single shot temporal action detection"

Connected through hyperedge HE-5 (induced by source paper "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"):
  with members:
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "learning spatio temporal representation with local and global diffusion"
    - "pointing novel objects in image captioning"
    - "to create what you tell generating videos from captions"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"

Connected through hyperedge HE-6 (induced by source paper "vireojd mm at activity detection in extended videos"):
  with members:
    - "exploring object relation in mean teacher for cross domain detection"
    - "learning spatio temporal representation with local and global diffusion"
    - "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "soft nms improving object detection with one line of code"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "temporal convolution based action proposal submission to activitynet 2017"
    - "temporal action localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-7 (induced by source paper "cross modal and hierarchical modeling of video and text"):
  with members:
    - "jointly localizing and describing events for dense video captioning"
    - "compressed video action recognition"
    - "learning robust visual semantic embeddings"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "temporal action detection with structured segment networks"
    - "dense captioning events in videos"
    - "localizing moments in video with natural language"
    - "real time action recognition with enhanced motion vector cnns"

Connected through hyperedge HE-8 (induced by source paper "eco efficient convolutional network for online video understanding"):
  with members:
    - "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"
    - "temporal segment networks for action recognition in videos"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "the kinetics human action video dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "end to end video level representation learning 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)