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: "radially distorted conjugate translations"
  Abstract: This paper introduces the first minimal solvers that jointly solve for affine-rectification and radial lens distortion from coplanar repeated patterns. Even with imagery from moderately distorted lenses, plane rectification using the pinhole camera model is inaccurate or invalid. The proposed solvers incorporate lens distortion into the camera model and extend accurate rectification to wide-angle imagery, which is now common from consumer cameras. The solvers are derived from constraints induced by the conjugate translations of an imaged scene plane, which are integrated with the division model for radial lens distortion. The hidden-variable trick with ideal saturation is used to reformulate the constraints so that the solvers generated by the Grobner-basis method are stable, small and fast. #R##N#The proposed solvers are used in a RANSAC-based estimator. Rectification and lens distortion are recovered from either one conjugately translated affine-covariant feature or two independently translated similarity-covariant features. Experiments confirm that RANSAC accurately estimates the rectification and radial distortion with very few iterations. The proposed solvers are evaluated against the state-of-the-art for affine rectification and radial distortion estimation.

Connected through hyperedge HE-1 (induced by source paper "minimal solvers for indoor uav positioning"):
  with members:
    - "a clever elimination strategy for efficient minimal solvers"
    - "a new solution to the relative orientation problem using only 3 points and the vertical direction"

Connected through hyperedge HE-2 (induced by source paper "homography from two orientation and scale covariant features"):
  with members:
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "a clever elimination strategy for efficient minimal solvers"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-3 (induced by source paper "minimal solvers for rectifying from radially distorted conjugate translations"):
  with members:
    - "minimal solvers for rectifying from radially distorted scales and change of scales"
    - "rectification from radially distorted scales"
    - "repeatability is not enough learning affine regions via discriminability"
    - "coplanar repeats by energy minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-4 (induced by source paper "minimal solvers for rectifying from radially distorted scales and change of scales"):
  with members:
    - "beyond gr obner bases basis selection for minimal solvers"
    - "repeatability is not enough learning affine regions via discriminability"
    - "coplanar repeats by energy minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-5 (induced by source paper "optimal multi view correction of local affine frames"):
  with members:
    - "multi class model fitting by energy minimization and mode seeking"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-6 (induced by source paper "recovering affine features from orientation and scale invariant ones"):
  with members:
    - "five point fundamental matrix estimation for uncalibrated cameras"
    - "a minimal solution for two view focal length estimation using two affine correspondences"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-7 (induced by source paper "rectification from radially distorted scales"):
  with members:
    - "beyond gr obner bases basis selection for minimal solvers"
    - "repeatability is not enough learning affine regions via discriminability"
    - "coplanar repeats by energy minimization"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-8 (induced by source paper "repeatability is not enough learning affine regions via discriminability"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "benchmarking 6dof urban visual localization in changing conditions"
    - "fine tuning cnn image retrieval with no human annotation"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "designing deep convolutional neural networks for continuous object orientation 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)