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: "s3d single shot multi span detector via fully 3d convolutional networks"
  Abstract: In this paper, we present a novel Single Shot multi-Span Detector for temporal activity detection in long, untrimmed videos using a simple end-to-end fully three-dimensional convolutional (Conv3D) network. Our architecture, named S3D, encodes the entire video stream and discretizes the output space of temporal activity spans into a set of default spans over different temporal locations and scales. At prediction time, S3D predicts scores for the presence of activity categories in each default span and produces temporal adjustments relative to the span location to predict the precise activity duration. Unlike many state-of-the-art systems that require a separate proposal and classification stage, our S3D is intrinsically simple and dedicatedly designed for single-shot, end-to-end temporal activity detection. When evaluating on THUMOS'14 detection benchmark, S3D achieves state-of-the-art performance and is very efficient and can operate at 1271 FPS.

Connected through hyperedge HE-1 (induced by source paper "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "segregated temporal assembly recurrent networks for weakly supervised multiple action detection"
    - "cascaded pyramid mining network for weakly supervised temporal action localization"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "end to end dense video captioning with masked transformer"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "ctap complementary temporal action proposal generation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "afo tad anchor free one stage detector for temporal action detection"):
  with members:
    - "gaussian temporal awareness networks for action localization"
    - "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"
    - "ctap complementary temporal action proposal generation"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-3 (induced by source paper "cmsn continuous multi stage network and variable margin cosine loss for temporal action proposal generation"):
  with members:
    - "bmn boundary matching network for temporal action proposal generation"
    - "arcface additive angular margin loss for deep face recognition"
    - "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"
    - "soft nms improving object detection with one line of code"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "deep point wise prediction for action temporal proposal"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "multi granularity generator for temporal action proposal"
    - "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"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "single shot temporal action detection"

Connected through hyperedge HE-5 (induced by source paper "learning sparse 2d temporal adjacent networks for temporal action localization"):
  with members:
    - "slowfast networks for video recognition"
    - "single shot temporal action detection"
    - "hacs human action clips and segments dataset for recognition and temporal localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "man moment alignment network for natural language moment retrieval via iterative graph adjustment"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "graph r cnn for scene graph generation"
    - "multilevel language and vision integration for text to clip retrieval"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "tan temporal aggregation network for dense multi label action recognition"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "actor and action video segmentation from a sentence"

Connected through hyperedge HE-7 (induced by source paper "tan temporal aggregation network for dense multi label action recognition"):
  with members:
    - "stereoscopic neural style transfer"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "deep motion boundary detection"
    - "decouple learning for parameterized image operators"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-8 (induced by source paper "learning to compose topic aware mixture of experts for zero shot video captioning"):
  with members:
    - "neural baby talk"
    - "universal neural machine translation for extremely low resource languages"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "decoupled novel object captioner"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)