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 sdp inference for fully connected crfs based on low rank decomposition"
  Abstract: Conditional Random Fields (CRFs) are one of the core technologies in computer vision, and have been applied to a wide variety of tasks. Conventional CRFs typically define edges between neighboring image pixels, resulting in a sparse graph over which inference can be performed efficiently. However, these CRFs fail to model more complex priors such as long-range contextual relationships. Fully-connected CRFs have thus been proposed. While there are efficient approximate inference methods for such CRFs, usually they are sensitive to initialization and make strong assumptions. In this work, we develop an efficient, yet general SDP algorithm for inference on fully-connected CRFs. The core of the proposed algorithm is a tailored quasi-Newton method, which solves a specialized SDP dual problem and takes advantage of the low-rank matrix approximation for fast computation. Experiments demonstrate that our method can be applied to fully-connected CRFs that could not previously be solved, such as those arising in pixel-level image co-segmentation.

Connected through hyperedge HE-1 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

Connected through hyperedge HE-2 (induced by source paper "efficient linear programming for dense crfs"):
  with members:
    - "efficient continuous relaxations for dense crf"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "efficient continuous relaxations for dense crf"):
  with members:
    - "principled parallel mean field inference for discrete random fields"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "block coordinate frank wolfe optimization for structural svms"
    - "map estimation of semi metric mrfs via hierarchical graph cuts"

Connected through hyperedge HE-4 (induced by source paper "biconvex relaxation for semidefinite programming in computer vision"):
  with members:
    - "a field guide to forward backward splitting with a fasta implementation"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "a fast semidefinite approach to solving binary quadratic problems"

Connected through hyperedge HE-5 (induced by source paper "learning data driven reflectance priors for intrinsic image decomposition"):
  with members:
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (induced by source paper "efficient semidefinite branch and cut for map mrf inference"):
  with members:
    - "crf learning with cnn features for image segmentation"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "higher order segmentation via multicuts"

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)