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: "efficient continuous relaxations for dense crf"
  Abstract: Dense conditional random fields (CRF) with Gaussian pairwise potentials have emerged as a popular framework for several computer vision applications such as stereo correspondence and semantic segmentation. By modeling long-range interactions, dense CRFs provide a more detailed labelling compared to their sparse counterparts. Variational inference in these dense models is performed using a filtering-based mean-field algorithm in order to obtain a fully-factorized distribution minimising the Kullback-Leibler divergence to the true distribution. In contrast to the continuous relaxation-based energy minimisation algorithms used for sparse CRFs, the mean-field algorithm fails to provide strong theoretical guarantees on the quality of its solutions. To address this deficiency, we show that it is possible to use the same filtering approach to speed-up the optimisation of several continuous relaxations. Specifically, we solve a convex quadratic programming (QP) relaxation using the efficient Frank-Wolfe algorithm. This also allows us to solve difference-of-convex relaxations via the iterative concave-convex procedure where each iteration requires solving a convex QP. Finally, we develop a novel divide-and-conquer method to compute the subgradients of a linear programming relaxation that provides the best theoretical bounds for energy minimisation. We demonstrate the advantage of continuous relaxations over the widely used mean-field algorithm on publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "on regularized losses for weakly supervised cnn segmentation"):
  with members:
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "efficient linear programming for dense crfs"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "efficient graph cut optimization for full crfs with quantized edges"):
  with members:
    - "memory efficient max flow for multi label submodular mrfs"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "the fast bilateral solver"
    - "fully connected deep structured networks"
    - "learning deep structured models"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-3 (induced by source paper "a projected gradient descent method for crf inference allowing end to end training of arbitrary pairwise potentials"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "laplacian reconstruction and refinement for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"
    - "structured prediction energy networks"

Connected through hyperedge HE-4 (induced by source paper "efficient linear programming for dense crfs"):
  with members:
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "duality between subgradient and conditional gradient methods"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)