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: "iteratively reweighted graph cut for multi label mrfs with non convex priors"
  Abstract: While widely acknowledged as highly effective in computer vision, multi-label MRFs with non-convex priors are difficult to optimize. To tackle this, we introduce an algorithm that iteratively approximates the original energy with an appropriately weighted surrogate energy that is easier to minimize. Our algorithm guarantees that the original energy decreases at each iteration. In particular, we consider the scenario where the global minimizer of the weighted surrogate energy can be obtained by a multi-label graph cut algorithm, and show that our algorithm then lets us handle of large variety of non-convex priors. We demonstrate the benefits of our method over state-of-the-art MRF energy minimization techniques on stereo and inpainting problems.

Connected through hyperedge HE-1 (induced by source paper "deeply supervised depth map super resolution as novel view synthesis"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep depth super resolution learning depth super resolution using deep convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "fast and differentiable message passing for stereo vision"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "generalized range moves"
    - "memory efficient max flow for multi label submodular mrfs"
    - "embedded real time stereo estimation via semi global matching on the gpu"
    - "conditional random fields as recurrent neural networks"
    - "loopy belief propagation for approximate inference an empirical study"
    - "learning graphical model parameters with approximate marginal inference"

Connected through hyperedge HE-3 (induced by source paper "memory efficient max flow for multi label submodular mrfs"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "opengm a c library for discrete graphical models"
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"

Connected through hyperedge HE-4 (induced by source paper "cvpaper challenge in 2015 a review of cvpr2015 and deepsurvey"):
  with members:
    - "semi supervised learning with explicit relationship regularization"
    - "understanding image virality"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "deep carving discovering visual attributes by carving deep neural nets"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "bilinear random projections for locality sensitive binary codes"
    - "learning multiple visual tasks while discovering their structure"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-5 (induced by source paper "deep depth super resolution learning depth super resolution using deep convolutional neural network"):
  with members:
    - "accurate image super resolution using very deep convolutional networks"
    - "deeply recursive convolutional network for image super resolution"

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)