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: "proflow learning to predict optical flow"
  Abstract: Temporal coherence is a valuable source of information in the context of optical flow estimation. However, finding a suitable motion model to leverage this information is a non-trivial task. In this paper we propose an unsupervised online learning approach based on a convolutional neural network (CNN) that estimates such a motion model individually for each frame. By relating forward and backward motion these learned models not only allow to infer valuable motion information based on the backward flow, they also help to improve the performance at occlusions, where a reliable prediction is particularly useful. Moreover, our learned models are spatially variant and hence allow to estimate non-rigid motion per construction. This, in turns, allows to overcome the major limitation of recent rigidity-based approaches that seek to improve the estimation by incorporating additional stereo/SfM constraints. Experiments demonstrate the usefulness of our new approach. They not only show a consistent improvement of up to 27% for all major benchmarks (KITTI 2012, KITTI 2015, MPI Sintel) compared to a baseline without prediction, they also show top results for the MPI Sintel benchmark -- the one of the three benchmarks that contains the largest amount of non-rigid motion.

Connected through hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "dgc net dense geometric correspondence network"

Connected through hyperedge HE-2 (induced by source paper "airsim drone racing lab"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "flying through a narrow gap using neural network an end to end planning and control approach"
    - "event based vision a survey"
    - "flightgoggles photorealistic sensor simulation for perception driven robotics using photogrammetry and virtual reality"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "deep drone racing learning agile flight in dynamic environments"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"

Connected through hyperedge HE-3 (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"
    - "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"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"

Connected through hyperedge HE-4 (induced by source paper "selflow self supervised learning of optical flow"):
  with members:
    - "self supervised visual feature learning with deep neural networks a survey"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "optical flow in mostly rigid scenes"
    - "accurate optical flow via direct cost volume processing"

Connected through hyperedge HE-5 (induced by source paper "a fusion approach for multi frame optical flow estimation"):
  with members:
    - "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"
    - "cnn based patch matching for optical flow with thresholded hinge loss"
    - "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)