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: "complexity of discrete energy minimization problems"
  Abstract: Discrete energy minimization is widely-used in computer vision and machine learning for problems such as MAP inference in graphical models. The problem, in general, is notoriously intractable, and finding the global optimal solution is known to be NP-hard. However, is it possible to approximate this problem with a reasonable ratio bound on the solution quality in polynomial time? We show in this paper that the answer is no. Specifically, we show that general energy minimization, even in the 2-label pairwise case, and planar energy minimization with three or more labels are exp-APX-complete. This finding rules out the existence of any approximation algorithm with a sub-exponential approximation ratio in the input size for these two problems, including constant factor approximations. Moreover, we collect and review the computational complexity of several subclass problems and arrange them on a complexity scale consisting of three major complexity classes -- PO, APX, and exp-APX, corresponding to problems that are solvable, approximable, and inapproximable in polynomial time. Problems in the first two complexity classes can serve as alternative tractable formulations to the inapproximable ones. This paper can help vision researchers to select an appropriate model for an application or guide them in designing new algorithms.

Connected through hyperedge HE-1 (induced by source paper "lifting vectorial variational problems a natural formulation based on geometric measure theory and discrete exterior calculus"):
  with members:
    - "functional maps representation on product manifolds"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "sublabel accurate discretization of nonconvex free discontinuity problems"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "sublabel accurate relaxation of nonconvex energies"

Connected through hyperedge HE-2 (induced by source paper "guaranteed parameter estimation for discrete energy minimization"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "dual coordinate solvers for large scale structural svms"
    - "block coordinate frank wolfe optimization for structural svms"

Connected through hyperedge HE-3 (induced by source paper "end to end training of hybrid cnn crf models for stereo"):
  with members:
    - "sublabel accurate convex relaxation of vectorial multilabel energies"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "solving dense image matching in real time using discrete continuous optimization"
    - "semantic image segmentation via deep parsing network"
    - "computing the stereo matching cost with a convolutional neural network"
    - "efficient likelihood learning of a generic cnn crf model for semantic segmentation"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "conditional random fields as recurrent neural networks"

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)