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: "temporal activity detection in untrimmed videos with recurrent neural networks"
  Abstract: This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed. As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way. Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network's output to achieve a better results on the temporally localization of activities on the videos. The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.

Connected through hyperedge HE-1 (induced by source paper "two stream region convolutional 3d network for temporal activity detection"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tube convolutional neural network t cnn for action detection in videos"

Connected through hyperedge HE-2 (induced by source paper "graph convolutional networks for temporal action localization"):
  with members:
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "end to end learning of motion representation for video understanding"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "weakly supervised dense event captioning in videos"
    - "person re identification with deep similarity guided graph neural network"
    - "adaptive sampling towards fast graph representation learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "videos as space time region graphs"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised action localization with background modeling"):
  with members:
    - "autoloc weakly supervised temporal action localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cascaded boundary regression for temporal action detection"
    - "temporal context network for activity localization in videos"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-4 (induced by source paper "simple vs complex temporal recurrences for video saliency prediction"):
  with members:
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "youtube vos sequence to sequence video object segmentation"
    - "excitation backprop for rnns"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"

Connected through hyperedge HE-5 (induced by source paper "video summarization via actionness ranking"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "temporal action detection with structured segment networks"
    - "video summarization with attention based encoder decoder networks"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "actionness estimation using hybrid fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "sa net deep neural network for robot trajectory recognition from rgb d streams"):
  with members:
    - "merging in congested freeway traffic using multipolicy decision making and passive actor critic learning"
    - "yolov3 an incremental improvement"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "maximum entropy deep inverse reinforcement learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-7 (induced by source paper "similarity r c3d for few shot temporal activity detection"):
  with members:
    - "a generative approach to zero shot and few shot action recognition"
    - "autoloc weakly supervised temporal action localization"
    - "visual data synthesis via gan for zero shot video classification"
    - "temporal action detection with structured segment networks"
    - "prototypical networks for few shot learning"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-8 (induced by source paper "joint event detection and description in continuous video streams"):
  with members:
    - "dense captioning events in videos"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "tall temporal activity localization via language query"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "localizing moments in video with natural language"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "dense captioning with joint inference and visual context"

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)