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: "semi global stereo matching with surface orientation priors"
  Abstract: Semi-Global Matching (SGM) is a widely-used efficient stereo matching technique. It works well for textured scenes, but fails on untextured slanted surfaces due to its fronto-parallel smoothness assumption. To remedy this problem, we propose a simple extension, termed SGM-P, to utilize precomputed surface orientation priors. Such priors favor different surface slants in different 2D image regions or 3D scene regions and can be derived in various ways. In this paper we evaluate plane orientation priors derived from stereo matching at a coarser resolution and show that such priors can yield significant performance gains for difficult weakly-textured scenes. We also explore surface normal priors derived from Manhattan-world assumptions, and we analyze the potential performance gains using oracle priors derived from ground-truth data. SGM-P only adds a minor computational overhead to SGM and is an attractive alternative to more complex methods employing higher-order smoothness terms.

Connected through hyperedge HE-1 (induced by source paper "deep depth prior for multi view stereo"):
  with members:
    - "a bayesian perspective on the deep image prior"
    - "learned collaborative stereo refinement"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "deepmvs learning multi view stereopsis"
    - "continuous 3d label stereo matching using local expansion moves"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "pyramid stereo matching network"

Connected through hyperedge HE-2 (induced by source paper "irs a large synthetic indoor robotics stereo dataset for disparity and surface normal estimation"):
  with members:
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast disparity estimation using dense networks"
    - "pyramid stereo matching network"
    - "single shot object detection with enriched semantics"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-3 (induced by source paper "efficient surface aware semi global matching with multi view plane sweep sampling"):
  with members:
    - "real time on board obstacle avoidance for uavs based on embedded stereo vision"
    - "deep cross domain building extraction for selective depth estimation from oblique aerial imagery"

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)