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 hybrid algorithm for convex semidefinite optimization"
  Abstract: We present a hybrid algorithm for optimizing a convex, smooth function over the cone of positive semidefinite matrices. Our algorithm converges to the global optimal solution and can be used to solve general large-scale semidefinite programs and hence can be readily applied to a variety of machine learning problems. We show experimental results on three machine learning problems (matrix completion, metric learning, and sparse PCA) . Our approach outperforms state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "bilinear factorization for low rank sdp learning"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "a direct formulation for sparse pca using semidefinite programming"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "block coordinate frank wolfe optimization for structural svms"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "robust learning from noisy side information by semidefinite programming"):
  with members:
    - "taking human out of learning applications a survey on automated machine learning"
    - "scalable robust matrix factorization with nonconvex loss"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "large scale binary quadratic optimization using semidefinite relaxation and applications"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "opengm a c library for discrete graphical models"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-5 (induced by source paper "a unified optimization view on generalized matching pursuit and frank wolfe"):
  with members:
    - "block coordinate frank wolfe optimization for structural svms"
    - "a geometric analysis of subspace clustering with outliers"
    - "sparse recovery with orthogonal matching pursuit under rip"

Connected through hyperedge HE-6 (induced by source paper "greedy algorithms for cone constrained optimization with convergence guarantees"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-7 (induced by source paper "scalable nuclear norm minimization by subspace pursuit proximal riemannian gradient"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "efficient and practical stochastic subgradient descent for nuclear norm regularization"
    - "a riemannian geometry for low rank matrix completion"
    - "large scale convex minimization with a low rank constraint"
    - "low rank matrix recovery from errors and erasures"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "efficient semidefinite branch and cut for map mrf inference"):
  with members:
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "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"

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)