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: "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"
  Abstract: Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "generating human action videos by coupling 3d game engines and probabilistic graphical models"):
  with members:
    - "pose guided human video generation"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "skeleton aided articulated motion generation"
    - "attentive semantic video generation using captions"
    - "carla an open urban driving simulator"
    - "temporal segment networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "actionvlad learning spatio temporal aggregation for action classification"):
  with members:
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "object detection from video tubelets with convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"

Connected through hyperedge HE-4 (induced by source paper "learnable pooling with context gating for video classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "aggregating frame level features for large scale video classification"
    - "the monkeytyping solution to the youtube 8m video understanding challenge"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "squeeze and excitation networks"
    - "temporal modeling approaches for large scale youtube 8m video understanding"
    - "a bag of words equivalent recurrent neural network for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "chained multi stream networks exploiting pose motion and appearance for action classification and detection"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "improved dense trajectory with cross streams"
    - "orientation boosted voxel nets for 3d object recognition"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "long term temporal convolutions for action recognition"

Connected through hyperedge HE-6 (induced by source paper "learning without prejudice avoiding bias in webly supervised action recognition"):
  with members:
    - "the thumos challenge on action recognition for videos in the wild"
    - "temporal segment networks towards good practices for deep action recognition"
    - "yfcc100m the new data in multimedia research"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "webly supervised learning of convolutional networks"
    - "a data driven approach for tag refinement and localization in web videos"
    - "unsupervised learning of video representations using lstms"
    - "automatic concept discovery from parallel text and visual corpora"

Connected through hyperedge HE-7 (induced by source paper "procedural generation of videos to train deep action recognition networks"):
  with members:
    - "how useful is photo realistic rendering for visual learning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "play and learn using video games to train computer vision models"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "learning physical intuition of block towers by example"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-8 (induced by source paper "asynchronous temporal fields for action recognition"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "learning visual storylines with skipping recurrent neural networks"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "towards weakly supervised action localization"
    - "spot on action localization from pointly supervised proposals"
    - "end to end learning of action detection from frame glimpses in videos"
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"

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)