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: "fast optical flow using dense inverse search"
  Abstract: Most recent works in optical flow extraction focus on the accuracy and neglect the time complexity. However, in real-life visual applications, such as tracking, activity detection and recognition, the time complexity is critical. #R##N#We propose a solution with very low time complexity and competitive accuracy for the computation of dense optical flow. It consists of three parts: 1) inverse search for patch correspondences; 2) dense displacement field creation through patch aggregation along multiple scales; 3) variational refinement. At the core of our Dense Inverse Search-based method (DIS) is the efficient search of correspondences inspired by the inverse compositional image alignment proposed by Baker and Matthews in 2001. #R##N#DIS is competitive on standard optical flow benchmarks with large displacements. DIS runs at 300Hz up to 600Hz on a single CPU core, reaching the temporal resolution of human's biological vision system. It is order(s) of magnitude faster than state-of-the-art methods in the same range of accuracy, making DIS ideal for visual applications.

Connected through hyperedge HE-1 (induced by source paper "learning the sense of touch in simulation a sim to real strategy for vision based tactile sensing"):
  with members:
    - "ground truth force distribution for learning based tactile sensing a finite element approach"
    - "mat multi fingered adaptive tactile grasping via deep reinforcement learning"
    - "tactile based insertion for dense box packing"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "robust learning of tactile force estimation through robot interaction"
    - "transfer learning for vision based tactile sensing"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "dance with flow two in one stream action detection"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "videocapsulenet a simplified network for action detection"
    - "a better baseline for ava"
    - "actor centric relation network"
    - "modulating early visual processing by language"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (induced by source paper "temporally consistent depth prediction with flow guided memory units"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "learning blind video temporal consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "deformable convolutional networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-4 (induced by source paper "efficient video semantic segmentation with labels propagation and refinement"):
  with members:
    - "panoptic feature pyramid networks"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "dynamic video segmentation network"
    - "low latency video semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "not all pixels are equal difficulty aware semantic segmentation via deep layer cascade"
    - "icnet for real time semantic segmentation on high resolution images"
    - "semantic video cnns through representation warping"

Connected through hyperedge HE-5 (induced by source paper "street scene a new dataset and evaluation protocol for video anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "energy based models for video anomaly detection"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning for optical flow estimation using pyramid convolution lstm"):
  with members:
    - "hidden two stream convolutional networks for action recognition"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "deep autoencoders with value at risk thresholding for unsupervised anomaly detection"):
  with members:
    - "background subtraction in real applications challenges current models and future directions"
    - "learning multi scale features for foreground segmentation"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "an overview of robust subspace recovery"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "a deep convolutional neural network for background subtraction"
    - "robust online matrix factorization for dynamic background subtraction"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-8 (induced by source paper "degraf flow extending degraf features for accurate and efficient sparse to dense optical flow estimation"):
  with members:
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow with semantic segmentation and localized layers"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "faster and better a machine learning approach to corner detection"

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)