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: "probflow joint optical flow and uncertainty estimation"
  Abstract: Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To that end, post-hoc confidence measures have been introduced to assess the per-pixel reliability of the flow. We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly predicts optical flow and its underlying uncertainty. Starting from common energy-based formulations, we rely on the corresponding posterior distribution of the flow given the images. We derive a variational inference scheme based on mean field, which incorporates best practices from energy minimization. An uncertainty measure is obtained along the flow at every pixel as the (marginal) entropy of the variational distribution. We demonstrate the flexibility of our probabilistic approach by applying it to two different energies and on two benchmarks. We not only obtain flow results that are competitive with the underlying energy minimization approach, but also a reliable uncertainty measure that significantly outperforms existing post-hoc approaches.

Connected through hyperedge HE-1 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"):
  with members:
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "deep rigid instance scene flow"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "deep visual domain adaptation a survey"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-2 (induced by source paper "learning representations for predicting future activities"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "stochastic adversarial video prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "predicting action tubes"
    - "predicting deeper into the future of semantic segmentation"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-3 (induced by source paper "hierarchical discrete distribution decomposition for match density estimation"):
  with members:
    - "lightweight probabilistic deep networks"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "segstereo exploiting semantic information for disparity estimation"
    - "layer structured 3d scene inference via view synthesis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pyramid stereo matching network"

Connected through hyperedge HE-4 (induced by source paper "eng end to end neural geometry for robust depth and pose estimation using cnns"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"
    - "semi supervised deep learning for monocular depth map prediction"

Connected through hyperedge HE-5 (induced by source paper "flowfields accurate optical flow correspondences meet robust interpolation"):
  with members:
    - "optical flow in mostly rigid scenes"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow with semantic segmentation and localized layers"

Connected through hyperedge HE-6 (induced by source paper "uncertainty estimates and multi hypotheses networks for optical flow"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "demon depth and motion network for learning monocular stereo"
    - "learning 3d object categories by looking around them"
    - "photographic image synthesis with cascaded refinement networks"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-7 (induced by source paper "stochastic variational inference with gradient linearization"):
  with members:
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "variational inference for monte carlo objectives"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "the fast bilateral solver"
    - "denoising criterion for variational auto encoding framework"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "adam a method for stochastic optimization"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-8 (induced by source paper "sceneflowfields dense interpolation of sparse scene flow correspondences"):
  with members:
    - "combining stereo disparity and optical flow for basic scene flow"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "a continuous optimization approach for efficient and accurate scene flow"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "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)