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: "dynamic computational time for visual attention"
  Abstract: We propose a dynamic computational time model to accelerate the average processing time for recurrent visual attention (RAM). Rather than attention with a fixed number of steps for each input image, the model learns to decide when to stop on the fly. To achieve this, we add an additional continue/stop action per time step to RAM and use reinforcement learning to learn both the optimal attention policy and stopping policy. The modification is simple but could dramatically save the average computational time while keeping the same recognition performance as RAM. Experimental results on CUB-200-2011 and Stanford Cars dataset demonstrate the dynamic computational model can work effectively for fine-grained image recognition.The source code of this paper can be obtained from this https URL

Connected through hyperedge HE-1 (induced by source paper "channel interaction networks for fine grained image categorization"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "compact generalized non local network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "classification is a strong baseline for deep metric learning"
    - "learning to navigate for fine grained classification"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-3 (induced by source paper "beyond the camera neural networks in world coordinates"):
  with members:
    - "deep online video stabilization"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "what actions are needed for understanding human actions in videos"
    - "deformable convolutional networks"
    - "sfm net learning of structure and motion from video"

Connected through hyperedge HE-4 (induced by source paper "looking fast and slow memory guided mobile video object detection"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "object detection in video with spatiotemporal sampling networks"
    - "depth adaptive computational policies for efficient visual tracking"
    - "towards high performance video object detection for mobiles"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-5 (induced by source paper "tell the difference fine grained visual descriptor via a discriminating referee"):
  with members:
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "style transfer from non parallel text by cross alignment"
    - "improved image captioning via policy gradient optimization of spider"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "detecting visual relationships with deep relational networks"
    - "dense captioning events in videos"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "reasoning about fine grained attribute phrases using reference games"

Connected through hyperedge HE-6 (induced by source paper "cross x learning for fine grained visual categorization"):
  with members:
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "sampling matters in deep embedding learning"
    - "deep layer aggregation"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "localizing by describing attribute guided attention localization for fine grained recognition"

Connected through hyperedge HE-7 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep texture manifold for ground terrain recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"

Connected through hyperedge HE-8 (induced by source paper "toward runtime throttleable neural networks"):
  with members:
    - "generalized ternary connect end to end learning and compression of multiplication free deep neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "skipnet learning dynamic routing in convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sgdr stochastic gradient descent with restarts"

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)