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: "dynamonet dynamic action and motion network"
  Abstract: In this paper, we are interested in self-supervised learning the motion cues in videos using dynamic motion filters for a better motion representation to finally boost human action recognition in particular. Thus far, the vision community has focused on spatio-temporal approaches using standard filters, rather we here propose dynamic filters that adaptively learn the video-specific internal motion representation by predicting the short-term future frames. We name this new motion representation, as dynamic motion representation (DMR) and is embedded inside of 3D convolutional network as a new layer, which captures the visual appearance and motion dynamics throughout entire video clip via end-to-end network learning. Simultaneously, we utilize these motion representation to enrich video classification. We have designed the frame prediction task as an auxiliary task to empower the classification problem. With these overall objectives, to this end, we introduce a novel unified spatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes the video classification and learning motion representation by predicting future frames as a multi-task learning problem. We conduct experiments on challenging human action datasets: Kinetics 400, UCF101, HMDB51. The experiments using the proposed DynamoNet show promising results on all the datasets.

Connected through hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"
    - "video representation learning by dense predictive coding"

Connected through hyperedge HE-2 (induced by source paper "don t forget the past recurrent depth estimation from monocular video"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"
    - "confidence propagation through cnns for guided sparse depth regression"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"

Connected through hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models"):
  with members:
    - "backprop diffusion is biologically plausible"
    - "decoupled greedy learning of cnns"
    - "putting an end to end to end gradient isolated learning of representations"
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "rethinking imagenet pre training"
    - "learning visual question answering by bootstrapping hard attention"
    - "massively parallel video networks"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-4 (induced by source paper "sth spatio temporal hybrid convolution for efficient action recognition"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "learning spatio temporal representation with local and global diffusion"
    - "eco efficient convolutional network for online video understanding"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "motion feature network fixed motion filter for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-5 (induced by source paper "spin a high speed high resolution vision dataset for tracking and action recognition in ping pong"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "tracking noisy targets a review of recent object tracking approaches"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "video action transformer network"
    - "representation flow for action recognition"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "large scale holistic video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "distinit learning video representations without a single labeled video"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-7 (induced by source paper "holistic large scale video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "hacs human action clips and segments dataset for recognition and temporal localization"

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)