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: "coplanar repeats by energy minimization"
  Abstract: This paper proposes an automated method to detect, group and rectify arbitrarily-arranged coplanar repeated elements via energy minimization. The proposed energy functional combines several features that model how planes with coplanar repeats are projected into images and captures global interactions between different coplanar repeat groups and scene planes. An inference framework based on a recent variant of $\alpha$-expansion is described and fast convergence is demonstrated. We compare the proposed method to two widely-used geometric multi-model fitting methods using a new dataset of annotated images containing multiple scene planes with coplanar repeats in varied arrangements. The evaluation shows a significant improvement in the accuracy of rectifications computed from coplanar repeats detected with the proposed method versus those detected with the baseline methods.

Connected through hyperedge HE-1 (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"
    - "radially distorted conjugate translations"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-2 (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"
    - "radially distorted conjugate translations"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-3 (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"
    - "radially distorted conjugate translations"
    - "tilt transform invariant low rank textures"

Connected through hyperedge HE-4 (induced by source paper "radially distorted conjugate translations"):
  with members:
    - "tilt transform invariant low rank textures"

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)