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: "ddflow learning optical flow with unlabeled data distillation"
  Abstract: We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on hand-crafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.

Connected through hyperedge HE-1 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "unsupervised scale consistent depth and ego motion learning from monocular video"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "deep declarative networks a new hope"
    - "meta learning with implicit gradients"
    - "selflow self supervised learning of optical flow"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-2 (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"
    - "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"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-3 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge"):
  with members:
    - "learning to adapt for stereo"
    - "sense a shared encoder network for scene flow estimation"
    - "guided stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "real time semantic stereo matching"
    - "deep rigid instance scene flow"
    - "real time self adaptive deep stereo"

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"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"
    - "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 "the five elements of flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "sense a shared encoder network for scene flow estimation"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "selflow self supervised learning of optical flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "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"
    - "hierarchical discrete distribution decomposition for match density 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)