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: "densenet for dense flow"
  Abstract: Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision. We extend current DenseNet to a fully convolutional network to learn motion estimation in an unsupervised manner. Evaluation results on three standard benchmarks demonstrate that DenseNet is a better fit than other widely adopted CNN architectures for optical flow estimation.

Connected through hyperedge HE-1 (induced by source paper "autodispnet improving disparity estimation with automl"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "graph hypernetworks for neural architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "learning to design rna"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "segmentation aware convolutional networks using local attention masks"
    - "optical flow in mostly rigid scenes"

Connected through hyperedge HE-3 (induced by source paper "improved optical flow for gesture based human robot interaction"):
  with members:
    - "deep optical flow estimation via multi scale correspondence structure learning"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "aggregated residual transformations for deep neural networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "densely connected pyramid dehazing network"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "gated fusion network for single image dehazing"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "face synthesis from visual attributes via sketch using conditional vaes and gans"
    - "residual dense network for image super resolution"
    - "context encoding for semantic segmentation"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "towards universal representation for unseen action recognition"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "alternative semantic representations for zero shot human action recognition"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "the kinetics human action video dataset"
    - "guided optical flow learning"
    - "image de raining using a conditional generative adversarial network"
    - "spatial aware object embeddings for zero shot localization and classification of actions"

Connected through hyperedge HE-6 (induced by source paper "random temporal skipping for multirate video analysis"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "towards universal representation for unseen action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-7 (induced by source paper "learning optical flow via dilated networks and occlusion reasoning"):
  with members:
    - "video enhancement with task oriented flow"
    - "hidden two stream convolutional networks for action recognition"
    - "occlusion aware unsupervised learning of optical flow"
    - "dilated residual networks"
    - "guided optical flow learning"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-8 (induced by source paper "conditional prior networks for optical flow"):
  with members:
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "fast optical flow using dense inverse search"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

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)