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: "cross scale cost aggregation for stereo matching"
  Abstract: Human beings process stereoscopic correspondence across multiple scales. However, this bio-inspiration is ignored by state-of-the-art cost aggregation methods for dense stereo correspondence. In this paper, a generic cross-scale cost aggregation framework is proposed to allow multi-scale interaction in cost aggregation. We firstly reformulate cost aggregation from a unified optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Then, an inter-scale regularizer is introduced into optimization and solving this new optimization problem leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation methods can be integrated into the proposed general framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, KITTI and New Tsukuba datasets.

Connected through hyperedge HE-1 (induced by source paper "deep stereo matching with explicit cost aggregation sub architecture"):
  with members:
    - "accurate optical flow via direct cost volume processing"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "convolutional two stream network fusion for video action recognition"
    - "computing the stereo matching cost with a convolutional neural network"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-2 (induced by source paper "cascaded multi scale and multi dimension convolutional neural network for stereo matching"):
  with members:
    - "widening siamese architectures for stereo matching"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-3 (induced by source paper "low level vision by consensus in a spatial hierarchy of regions"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "from shading to local shape"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "continuous markov random fields for robust stereo 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)