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: "rnn fisher vectors for action recognition and image annotation"
  Abstract: Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences. We demonstrate that RNNs can be effectively used in order to encode sequences and provide effective representations. The methodology we use is based on Fisher Vectors, where the RNNs are the generative probabilistic models and the partial derivatives are computed using backpropagation. State of the art results are obtained in two central but distant tasks, which both rely on sequences: video action recognition and image annotation. We also show a surprising transfer learning result from the task of image annotation to the task of video action recognition.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "deep multimodal image text embeddings for automatic cross media retrieval"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "stacked cross attention for image text matching"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "twitter100k a real world dataset for weakly supervised cross media retrieval"
    - "dual path convolutional image text embedding with instance loss"
    - "leveraging visual question answering for image caption ranking"
    - "aggregated residual transformations for deep neural networks"
    - "instance aware image and sentence matching with selective multimodal lstm"

Connected through hyperedge HE-3 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-4 (induced by source paper "learning visual relation priors for image text matching and image captioning with neural scene graph generators"):
  with members:
    - "relational reasoning using prior knowledge for visual captioning"
    - "rethinking visual relationships for high level image understanding"
    - "object driven text to image synthesis via adversarial training"
    - "vrr vg refocusing visually relevant relationships"
    - "scene graph reasoning with prior visual relationship for visual question answering"
    - "stacked cross attention for image text matching"
    - "multi modal learning with prior visual relation reasoning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-5 (induced by source paper "3 d personvlad learning deep global representations for video based person reidentification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "scalable person re identification on supervised smoothed manifold"

Connected through hyperedge HE-6 (induced by source paper "interaction aware spatio temporal pyramid attention networks for action classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attend and interact higher order object interactions for video understanding"
    - "attention is all you need"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "attention clusters purely attention based local feature integration for video classification"
    - "attentional pooling for action recognition"

Connected through hyperedge HE-7 (induced by source paper "predicting visual features from text for image and video caption retrieval"):
  with members:
    - "end to end concept word detection for video captioning retrieval and question answering"
    - "generative adversarial text to image synthesis"
    - "the imagenet shuffle reorganized pre training for video event detection"
    - "learning joint representations of videos and sentences with web image search"
    - "image captioning with deep bidirectional lstms"
    - "learning deep structure preserving image text embeddings"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "eventnet a large scale structured concept library for complex event detection in video"

Connected through hyperedge HE-8 (induced by source paper "eco efficient convolutional network for online video understanding"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "temporal segment networks for action recognition in videos"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "the kinetics human action video dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "end to end video level representation learning for action recognition"

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)