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: "gaussian temporal awareness networks for action localization"
  Abstract: Temporally localizing actions in a video is a fundamental challenge in video understanding. Most existing approaches have often drawn inspiration from image object detection and extended the advances, e.g., SSD and Faster R-CNN, to produce temporal locations of an action in a 1D sequence. Nevertheless, the results can suffer from robustness problem due to the design of predetermined temporal scales, which overlooks the temporal structure of an action and limits the utility on detecting actions with complex variations. In this paper, we propose to address the problem by introducing Gaussian kernels to dynamically optimize temporal scale of each action proposal. Specifically, we present Gaussian Temporal Awareness Networks (GTAN) --- a new architecture that novelly integrates the exploitation of temporal structure into an one-stage action localization framework. Technically, GTAN models the temporal structure through learning a set of Gaussian kernels, each for a cell in the feature maps. Each Gaussian kernel corresponds to a particular interval of an action proposal and a mixture of Gaussian kernels could further characterize action proposals with various length. Moreover, the values in each Gaussian curve reflect the contextual contributions to the localization of an action proposal. Extensive experiments are conducted on both THUMOS14 and ActivityNet v1.3 datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, GTAN achieves 1.9% and 1.1% improvements in mAP on testing set of the two datasets.

Connected through hyperedge HE-1 (induced by source paper "sf net single frame supervision for temporal action localization"):
  with members:
    - "3c net category count and center loss for weakly supervised action localization"
    - "action recognition from single timestamp supervision in untrimmed videos"
    - "weakly supervised action localization with background modeling"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "slowfast networks for video recognition"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "bsn boundary sensitive network for temporal action proposal generation"

Connected through hyperedge HE-2 (induced by source paper "g tad sub graph localization for temporal action detection"):
  with members:
    - "learning video representations from correspondence proposals"
    - "graph convolutional networks for temporal action localization"
    - "mesh r cnn"
    - "bmn boundary matching network for temporal action proposal generation"
    - "deepgcns can gcns go as deep as cnns"
    - "point clouds learning with attention based graph convolution networks"
    - "diagnosing error in temporal action detectors"
    - "multi granularity generator for temporal action proposal"

Connected through hyperedge HE-3 (induced by source paper "a context aware loss function for action spotting in soccer videos"):
  with members:
    - "golfdb a video database for golf swing sequencing"
    - "pan tilt zoom slam for sports videos"
    - "bmn boundary matching network for temporal action proposal generation"
    - "fine grained activity recognition in baseball videos"
    - "diagnosing error in temporal action detectors"
    - "temporal hockey action recognition via pose and optical flows"
    - "taskonomy disentangling task transfer learning"
    - "hitnet a neural network with capsules embedded in a hit or miss layer extended with hybrid data augmentation and ghost capsules"

Connected through hyperedge HE-4 (induced by source paper "afo tad anchor free one stage detector for temporal action detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "bmn boundary matching network for temporal action proposal generation"
    - "cornernet detecting objects as paired keypoints"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "ctap complementary temporal action proposal generation"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-5 (induced by source paper "decoupling localization and classification in single shot temporal action detection"):
  with members:
    - "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"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-6 (induced by source paper "background suppression network for weakly supervised temporal action localization"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-7 (induced by source paper "cross class relevance learning for temporal concept localization"):
  with members:
    - "non local netvlad encoding for video classification"
    - "label denoising with large ensembles of heterogeneous neural networks"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "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"
    - "learnable pooling with context gating for video classification"
    - "cdc convolutional de convolutional networks for precise temporal action localization in untrimmed videos"

Connected through hyperedge HE-8 (induced by source paper "fast learning of temporal action proposal via dense boundary generator"):
  with members:
    - "bmn boundary matching network for temporal action proposal generation"
    - "multi granularity generator for temporal action proposal"
    - "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"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"

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)