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: "on graduated optimization for stochastic non convex problems"
  Abstract: The graduated optimization approach, also known as the continuation method, is a popular heuristic to solving non-convex problems that has received renewed interest over the last decade. Despite its popularity, very little is known in terms of theoretical convergence analysis. In this paper we describe a new first-order algorithm based on graduated optimiza- tion and analyze its performance. We characterize a parameterized family of non- convex functions for which this algorithm provably converges to a global optimum. In particular, we prove that the algorithm converges to an {\epsilon}-approximate solution within O(1/\epsilon^2) gradient-based steps. We extend our algorithm and analysis to the setting of stochastic non-convex optimization with noisy gradient feedback, attaining the same convergence rate. Additionally, we discuss the setting of zero-order optimization, and devise a a variant of our algorithm which converges at rate of O(d^2/\epsilon^4).

Connected through hyperedge HE-1 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-2 (induced by source paper "graduated optimization of black box functions"):
  with members:
    - "scale free online learning"
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "an optimal algorithm for bandit and zero order convex optimization with two point feedback"
    - "a literature survey of benchmark functions for global optimisation problems"

Connected through hyperedge HE-3 (induced by source paper "learning to teach with dynamic loss functions"):
  with members:
    - "neural architecture optimization"
    - "interpretable and pedagogical examples"
    - "adversarial neural machine translation"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "learning to optimize"

Connected through hyperedge HE-4 (induced by source paper "activestereonet end to end self supervised learning for active stereo systems"):
  with members:
    - "stereonet guided hierarchical refinement for real time edge aware depth prediction"
    - "intel realsense stereoscopic depth cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning to teach"):
  with members:
    - "automated curriculum learning for neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "neural architecture search with reinforcement learning"
    - "the teaching dimension of linear learners"
    - "deep residual learning for image recognition"
    - "semi supervised sequence learning"

Connected through hyperedge HE-6 (induced by source paper "low rank bandit methods for high dimensional dynamic pricing"):
  with members:
    - "perishability of data dynamic pricing under varying coefficient models"
    - "online learning with low rank experts"
    - "contextual bandits with latent confounders an nmf approach"
    - "kernel based methods for bandit convex optimization"
    - "low rank bandits with latent mixtures"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-7 (induced by source paper "online newton step algorithm with estimated gradient"):
  with members:
    - "online learning a comprehensive survey"
    - "stochastic optimization with bandit sampling"
    - "online learning to rank in stochastic click models"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "an incremental path following splitting method for linearly constrained nonconvex nonsmooth programs"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization 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)