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: "what have we learned from deep representations for action recognition"
  Abstract: As the success of deep models has led to their deployment in all areas of computer vision, it is increasingly important to understand how these representations work and what they are capturing. In this paper, we shed light on deep spatiotemporal representations by visualizing what two-stream models have learned in order to recognize actions in video. We show that local detectors for appearance and motion objects arise to form distributed representations for recognizing human actions. Key observations include the following. First, cross-stream fusion enables the learning of true spatiotemporal features rather than simply separate appearance and motion features. Second, the networks can learn local representations that are highly class specific, but also generic representations that can serve a range of classes. Third, throughout the hierarchy of the network, features become more abstract and show increasing invariance to aspects of the data that are unimportant to desired distinctions (e.g. motion patterns across various speeds). Fourth, visualizations can be used not only to shed light on learned representations, but also to reveal idiosyncracies of training data and to explain failure cases of the system.

Connected through hyperedge HE-1 (induced by source paper "domain specific priors and meta learning for low shot first person action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "how to train your maml"
    - "towards an unequivocal representation of actions"
    - "probabilistic model agnostic meta learning"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a simple neural network module for relational reasoning"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-2 (induced by source paper "paying more attention to motion attention distillation for learning video representations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deeply supervised salient object detection with short connections"
    - "end to end learning of motion representation for video understanding"
    - "on the effectiveness of task granularity for transfer learning"
    - "end to end multi task learning with attention"
    - "modality distillation with multiple stream networks for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "repair removing representation bias by dataset resampling"):
  with members:
    - "women also snowboard overcoming bias in captioning models"
    - "on the effectiveness of task granularity for transfer learning"
    - "recognition in terra incognita"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "the kinetics human action video dataset"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

Connected through hyperedge HE-4 (induced by source paper "explainable video action reasoning via prior knowledge and state transitions"):
  with members:
    - "referring relationships"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"
    - "joint discovery of object states and manipulation actions"
    - "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"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving action recognition using coded aperture videos"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "learning to anonymize faces for privacy preserving action detection"
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "diffusercam lensless single exposure 3d imaging"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "playing for data ground truth from computer games"
    - "convolutional two stream network fusion for video action recognition"

Connected through hyperedge HE-6 (induced by source paper "interpreting video features a comparison of 3d convolutional networks and convolutional lstm networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video time properties encoders and evaluation"
    - "methods for interpreting and understanding deep neural networks"
    - "sanity checks for saliency maps"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "excitation backprop for rnns"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-7 (induced by source paper "dynamics are important for the recognition of equine pain in video"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "recurrent convolutional neural network regression for continuous pain intensity estimation in video"
    - "temporal segment networks towards good practices for deep action recognition"
    - "videolstm convolves attends and flows for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "action recognition using visual attention"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "learning deep representations for video based intake gesture detection"):
  with members:
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "convolutional two stream network fusion for video action recognition"
    - "deep residual learning for image recognition"
    - "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"

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)