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: "dap3d net where what and how actions occur in videos"
  Abstract: Action parsing in videos with complex scenes is an interesting but challenging task in computer vision. In this paper, we propose a generic 3D convolutional neural network in a multi-task learning manner for effective Deep Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase, action localization, classification and attributes learning can be jointly optimized on our appearancemotion data via DAP3D-Net. For an upcoming test video, we can describe each individual action in the video simultaneously as: Where the action occurs, What the action is and How the action is performed. To well demonstrate the effectiveness of the proposed DAP3D-Net, we also contribute a new Numerous-category Aligned Synthetic Action dataset, i.e., NASA, which consists of 200; 000 action clips of more than 300 categories and with 33 pre-defined action attributes in two hierarchical levels (i.e., low-level attributes of basic body part movements and high-level attributes related to action motion). We learn DAP3D-Net using the NASA dataset and then evaluate it on our collected Human Action Understanding (HAU) dataset. Experimental results show that our approach can accurately localize, categorize and describe multiple actions in realistic videos.

Connected through hyperedge HE-1 (induced by source paper "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"):
  with members:
    - "pedx benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "densepose dense human pose estimation in the wild"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-2 (induced by source paper "learning conditional random fields with augmented observations for partially observed action recognition"):
  with members:
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "structured feature learning for pose estimation"
    - "convolutional two stream network fusion for video action recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "depth2action exploring embedded depth for large scale action recognition"):
  with members:
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "improving human activity recognition through ranking and re ranking"
    - "delving deeper into convolutional networks for learning video representations"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"
    - "depth extraction from videos using geometric context and occlusion boundaries"
    - "do less and achieve more training cnns for action recognition utilizing action images from the web"
    - "coupled depth learning"

Connected through hyperedge HE-4 (induced by source paper "efficient action detection in untrimmed videos via multi task learning"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "improving human action recognition by non action classification"
    - "end to end learning of action detection from frame glimpses in videos"
    - "instance aware semantic segmentation via multi task network cascades"
    - "deep residual learning for image recognition"
    - "deepstereo learning to predict new views from the world s imagery"
    - "objects2action classifying and localizing actions without any video example"

Connected through hyperedge HE-5 (induced by source paper "dave a unified framework for fast vehicle detection and annotation"):
  with members:
    - "a large scale car dataset for fine grained categorization and verification"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)