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: "action recognition with joint attention on multi level deep features"
  Abstract: We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level deep features from deep CNNs and feed them into 3d-convolutional network. After that we feed those feature cubes into our novel joint LSTM module to predict labels and to generate attention regularization. We evaluate our model on two challenging datasets: UCF101 and HMDB51. The results show that our model achieves the state-of-art by only using convolutional features.

Connected through hyperedge HE-1 (induced by source paper "generating question relevant captions to aid visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self critical reasoning for robust visual question answering"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "dynamic filtering with large sampling field for convnets"

Connected through hyperedge HE-2 (induced by source paper "dynamic filtering with large sampling field for convnets"):
  with members:
    - "segmentation aware convolutional networks using local attention masks"
    - "couplenet coupling global structure with local parts for object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "residual attention network for image classification"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-3 (induced by source paper "action recognition with coarse to fine deep feature integration and asynchronous fusion"):
  with members:
    - "generalized rank pooling for activity recognition"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "lattice long short term memory for human action recognition"
    - "motion representation with acceleration images"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "deeply fused nets"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video 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)