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: "minding the gaps for block frank wolfe optimization of structured svms"
  Abstract: In this paper, we propose several improvements on the block-coordinate Frank-Wolfe (BCFW) algorithm from Lacoste-Julien et al. (2013) recently used to optimize the structured support vector machine (SSVM) objective in the context of structured prediction, though it has wider applications. The key intuition behind our improvements is that the estimates of block gaps maintained by BCFW reveal the block suboptimality that can be used as an adaptive criterion. First, we sample objects at each iteration of BCFW in an adaptive non-uniform way via gapbased sampling. Second, we incorporate pairwise and away-step variants of Frank-Wolfe into the block-coordinate setting. Third, we cache oracle calls with a cache-hit criterion based on the block gaps. Fourth, we provide the first method to compute an approximate regularization path for SSVM. Finally, we provide an exhaustive empirical evaluation of all our methods on four structured prediction datasets.

Connected through hyperedge HE-1 (induced by source paper "curvature of feasible sets in offline and online optimization"):
  with members:
    - "introduction to online convex optimization"
    - "acceleration through optimistic no regret dynamics"
    - "faster rates for convex concave games"
    - "parameter free online learning via model selection"
    - "block coordinate frank wolfe optimization for structural svms"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-2 (induced by source paper "a flexible model for training action localization with varying levels of supervision"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "localizing actions from video labels and pseudo annotations"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "action tubelet detector for spatio temporal action localization"
    - "human action localization with sparse spatial supervision"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "map inference via block coordinate frank wolfe algorithm"):
  with members:
    - "efficient linear programming for dense crfs"
    - "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"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "block coordinate frank wolfe optimization for structural svms"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-4 (induced by source paper "revisiting projection free optimization for strongly convex constraint sets"):
  with members:
    - "introduction to online convex optimization"
    - "following the leader and fast rates in linear prediction curved constraint sets and other regularities"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "robust optimization for deep regression"
    - "on pairwise costs for network flow multi object tracking"
    - "projection free online learning"
    - "large scale convex minimization with a low rank constraint"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised learning of visual relations"):
  with members:
    - "learning from video and text via large scale discriminative clustering"
    - "grounding of textual phrases in images by reconstruction"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "a multipath network for object detection"
    - "visual relationship detection with language priors"
    - "captioning images with diverse objects"
    - "latent embeddings for zero shot classification"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-6 (induced by source paper "learning from video and text via large scale discriminative clustering"):
  with members:
    - "towards weakly supervised action localization"
    - "a dataset for movie description"
    - "unsupervised learning from narrated instruction videos"
    - "deep residual learning for image recognition"
    - "weakly supervised alignment of video with text"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "weakly supervised action labeling in videos under ordering constraints"

Connected through hyperedge HE-7 (induced by source paper "safe adaptive importance sampling"):
  with members:
    - "faster coordinate descent via adaptive importance sampling"
    - "approximate steepest coordinate descent"
    - "importance sampling for minibatches"
    - "safe screening with variational inequalities and its application to lasso"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-8 (induced by source paper "coordinate descent with bandit sampling"):
  with members:
    - "stochastic optimization with bandit sampling"
    - "faster coordinate descent via adaptive importance sampling"
    - "approximate steepest coordinate descent"
    - "importance sampling for minibatches"

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)