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: "saliency tubes visual explanations for spatio temporal convolutions"
  Abstract: Deep learning approaches have been established as the main methodology for video classification and recognition. Recently, 3-dimensional convolutions have been used to achieve state-of-the-art performance in many challenging video datasets. Because of the high level of complexity of these methods, as the convolution operations are also extended to additional dimension in order to extract features from them as well, providing a visualization for the signals that the network interpret as informative, is a challenging task. An effective notion of understanding the network's inner-workings would be to isolate the spatio-temporal regions on the video that the network finds most informative. We propose a method called Saliency Tubes which demonstrate the foremost points and regions in both frame level and over time that are found to be the main focus points of the network. We demonstrate our findings on widely used datasets for third-person and egocentric action classification and enhance the set of methods and visualizations that improve 3D Convolutional Neural Networks (CNNs) intelligibility.

Connected through hyperedge HE-1 (induced by source paper "learning class regularized features for action recognition"):
  with members:
    - "class feature pyramids for video explanation"
    - "visualizing and understanding generative adversarial networks extended abstract"
    - "large scale weakly supervised pre training for video action recognition"
    - "multi fiber networks for video recognition"
    - "explaining explanations an overview of interpretability of machine learning"
    - "analyzing human human interactions a survey"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "class feature pyramids for video explanation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "large scale weakly supervised pre training for video action recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "multigrain a unified image embedding for classes and instances"
    - "grounding visual explanations"
    - "multi fiber networks for video recognition"

Connected through hyperedge HE-3 (induced by source paper "gate shift networks for video action recognition"):
  with members:
    - "attentive spatio temporal representation learning for diving classification"
    - "video modeling with correlation networks"
    - "action recognition with spatial temporal discriminative filter banks"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "eco efficient convolutional network for online video understanding"

Connected through hyperedge HE-4 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"
    - "taskonomy disentangling task transfer learning"

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)