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: "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
  Abstract: Structural support vector machines (SSVMs) are amongst the best performing models for structured computer vision tasks, such as semantic image segmentation or human pose estimation. Training SSVMs, however, is computationally costly, because it requires repeated calls to a structured prediction subroutine (called \emph{max-oracle}), which has to solve an optimization problem itself, e.g. a graph cut. #R##N#In this work, we introduce a new algorithm for SSVM training that is more efficient than earlier techniques when the max-oracle is computationally expensive, as it is frequently the case in computer vision tasks. The main idea is to (i) combine the recent stochastic Block-Coordinate Frank-Wolfe algorithm with efficient hyperplane caching, and (ii) use an automatic selection rule for deciding whether to call the exact max-oracle or to rely on an approximate one based on the cached hyperplanes. #R##N#We show experimentally that this strategy leads to faster convergence to the optimum with respect to the number of requires oracle calls, and that this translates into faster convergence with respect to the total runtime when the max-oracle is slow compared to the other steps of the algorithm. #R##N#A publicly available C++ implementation is provided at this http URL .

Connected through hyperedge HE-1 (induced by source paper "map inference via block coordinate frank wolfe algorithm"):
  with members:
    - "efficient linear programming for dense crfs"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "a new look at reweighted message passing"
    - "submodular relaxation for inference in markov random fields"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "block coordinate frank wolfe optimization for structural svms"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "guaranteed parameter estimation for discrete energy minimization"):
  with members:
    - "complexity of discrete energy minimization problems"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "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 "trusting svm for piecewise linear cnns"):
  with members:
    - "reliably learning the relu in polynomial time"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "training neural networks without gradients a scalable admm approach"
    - "input convex neural networks"
    - "convexified convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "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"
    - "duality between subgradient and conditional gradient methods"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-5 (induced by source paper "minding the gaps for block frank wolfe optimization of structured svms"):
  with members:
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"
    - "reflection methods for user friendly submodular optimization"
    - "a novel frank wolfe algorithm analysis and applications to large scale svm training"
    - "block coordinate frank wolfe optimization for structural svms"
    - "suboptimal solution path algorithm for support vector machine"
    - "an exponential lower bound on the complexity of regularization paths"
    - "introduction to the conll 2000 shared task chunking"

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.LG(Machine Learning)