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: "aggregation of local parametric candidates with exemplar based occlusion handling for optical flow"
  Abstract: Handling all together large displacements, motion details and occlusions remains an open issue for reliable computation of optical flow in a video sequence. We propose a two-step aggregation paradigm to address this problem. The idea is to supply local motion candidates at every pixel in a first step, and then to combine them to determine the global optical flow field in a second step. We exploit local parametric estimations combined with patch correspondences and we experimentally demonstrate that they are sufficient to produce highly accurate motion candidates. The aggregation step is designed as the discrete optimization of a global regularized energy. The occlusion map is estimated jointly with the flow field throughout the two steps. We propose a generic exemplar-based approach for occlusion filling with motion vectors. We achieve state-of-the-art results in computer vision benchmarks, with particularly significant improvements in the case of large displacements and occlusions.

Connected through hyperedge HE-1 (induced by source paper "fast piecewise affine motion estimation without segmentation"):
  with members:
    - "optical flow with semantic segmentation and localized layers"
    - "joint optical flow and temporally consistent semantic segmentation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "sparsity based methods for overparameterized variational problems"

Connected through hyperedge HE-2 (induced by source paper "pyramidal gradient matching for optical flow estimation"):
  with members:
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-3 (induced by source paper "faldoi a new minimization strategy for large displacement variational optical flow"):
  with members:
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "patchbatch a batch augmented loss for optical flow"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-4 (induced by source paper "faldoi large displacement optical flow by astute initialization"):
  with members:
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-5 (induced by source paper "patchbatch a batch augmented loss for optical flow"):
  with members:
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "computing the stereo matching cost with a convolutional neural network"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "flownet learning optical flow with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deepmatching hierarchical deformable dense matching"
    - "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)