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: "variational depth from focus reconstruction"
  Abstract: This paper deals with the problem of reconstructing a depth map from a sequence of differently focused images, also known as depth from focus (DFF) or shape from focus. We propose to state the DFF problem as a variational problem, including a smooth but nonconvex data fidelity term and a convex nonsmooth regularization, which makes the method robust to noise and leads to more realistic depth maps. In addition, we propose to solve the nonconvex minimization problem with a linearized alternating directions method of multipliers, allowing to minimize the energy very efficiently. A numerical comparison to classical methods on simulated as well as on real data is presented.

Connected through hyperedge HE-1 (induced by source paper "application of preconditioned alternating direction method of multipliers in depth from focal stack"):
  with members:
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-2 (induced by source paper "deep depth from focus"):
  with members:
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "image based localization using lstms for structured feature correlation"
    - "pyramid scene parsing network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "flownet learning optical flow with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep depth inference using binocular and monocular cues"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "computing the stereo matching cost with a convolutional neural network"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional neural networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)