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: "cnn based patch matching for optical flow with thresholded hinge loss"
  Abstract: Learning based approaches have not yet achieved their full potential in optical flow estimation, where their performance still trails heuristic approaches. In this paper, we present a novel optical flow pipeline that uses patch-matching with CNN trained features at multiple scales. We show a novel way for calculating CNN based features for different scales, which performs better than existing methods. Furthermore, we introduce a new thresholded loss for Siamese networks and demonstrate that our novel loss performs clearly better than existing losses. It also allows to speed up training by a factor of 2 in our tests. Moreover, we discuss new ways of evaluating the robustness of trained features for the application of patch matching for optical flow. An interesting discovery in our paper is that low pass filtering feature maps can increase the robustness of features created by CNNs. We prove competitive performance of our approach by submitting it to the KITTI 2012 and KITTI 2015 evaluation portals and obtaining the best results on KITTI 2012 and the best for foreground objects in KITTI 2015.

Connected through hyperedge HE-1 (induced by source paper "physical cue based depth sensing by color coding with deaberration network"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fast feature extraction with cnns with pooling layers"
    - "deep ordinal regression network for monocular depth estimation"
    - "pyramid stereo matching network"
    - "random erasing data augmentation"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "semi supervised deep learning for monocular depth map prediction"

Connected through hyperedge HE-2 (induced by source paper "a fusion approach for multi frame optical flow estimation"):
  with members:
    - "proflow learning to predict optical flow"
    - "optical flow in mostly rigid scenes"
    - "accurate optical flow via direct cost volume processing"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "on human motion prediction using recurrent neural networks"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "learning image relations with contrast association networks"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "demon depth and motion network for learning monocular stereo"
    - "hadamard product for low rank bilinear pooling"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "learning to extract motion from videos in convolutional neural networks"
    - "fully trainable deep matching"
    - "improved stereo matching with constant highway networks and reflective confidence learning"

Connected through hyperedge HE-4 (induced by source paper "flownet 2 0 evolution of optical flow estimation with deep networks"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "fast optical flow using dense inverse search"
    - "stacked hourglass networks for human pose estimation"
    - "optical flow estimation using a spatial pyramid network"
    - "learning to extract motion from videos in convolutional neural networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"
    - "fully trainable deep matching"

Connected through hyperedge HE-5 (induced by source paper "optical flow requires multiple strategies but only one network"):
  with members:
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "dense optical flow prediction from a static image"
    - "patchbatch a batch augmented loss for optical flow"
    - "learning to compare image patches via convolutional neural networks"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

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)