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 dynamic image networks"
  Abstract: We introduce the concept of "dynamic image", a novel compact representation of videos useful for video analysis, particularly in combination with convolutional neural networks (CNNs). A dynamic image encodes temporal data such as RGB or optical flow videos by using the concept of `rank pooling'. The idea is to learn a ranking machine that captures the temporal evolution of the data and to use the parameters of the latter as a representation. When a linear ranking machine is used, the resulting representation is in the form of an image, which we call dynamic because it summarizes the video dynamics in addition of appearance. This is a powerful idea because it allows to convert any video to an image so that existing CNN models pre-trained for the analysis of still images can be immediately extended to videos. We also present an efficient and effective approximate rank pooling operator, accelerating standard rank pooling algorithms by orders of magnitude, and formulate that as a CNN layer. This new layer allows generalizing dynamic images to dynamic feature maps. We demonstrate the power of the new representations on standard benchmarks in action recognition achieving state-of-the-art performance.

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 "goal conditioned end to end visuomotor control for versatile skill primitives"):
  with members:
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "object centric forward modeling for model predictive control"
    - "good robot efficient reinforcement learning for multi step visual tasks via reward shaping"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "universal planning networks"
    - "reasoning about physical interactions with object oriented prediction and planning"

Connected through hyperedge HE-3 (induced by source paper "learning spatio temporal representations with temporal squeeze pooling"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "squeeze and excitation networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "beyond short snippets deep networks for video classification"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "2d convolutional neural networks for 3d digital breast tomosynthesis classification"):
  with members:
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "what have we learned from deep representations for action recognition"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised gaussian networks for action detection"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "what actions are needed for understanding human actions in videos"
    - "discriminatively learned hierarchical rank pooling networks"

Connected through hyperedge HE-7 (induced by source paper "inferring dynamic representations of facial actions from a still image"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a functional regression approach to facial landmark tracking"
    - "action anticipation by predicting future dynamic images"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

Connected through hyperedge HE-8 (induced by source paper "lstms can capture information beyond order"):
  with members:
    - "orderless recurrent models for multi label classification"
    - "multiple instance learning with graph neural networks"
    - "deep spectral clustering using dual autoencoder network"
    - "attention based deep multiple instance learning"
    - "learning and interpreting multi multi instance learning networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual motion gan for future flow embedded video prediction"
    - "multiple instance learning a survey of problem characteristics and applications"

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)