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 generic approach for escaping saddle points"
  Abstract: A central challenge to using first-order methods for optimizing nonconvex problems is the presence of saddle points. First-order methods often get stuck at saddle points, greatly deteriorating their performance. Typically, to escape from saddles one has to use second-order methods. However, most works on second-order methods rely extensively on expensive Hessian-based computations, making them impractical in large-scale settings. To tackle this challenge, we introduce a generic framework that minimizes Hessian based computations while at the same time provably converging to second-order critical points. Our framework carefully alternates between a first-order and a second-order subroutine, using the latter only close to saddle points, and yields convergence results competitive to the state-of-the-art. Empirical results suggest that our strategy also enjoys a good practical performance.

Connected through hyperedge HE-1 (induced by source paper "on gradient based learning in continuous games"):
  with members:
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "bandit learning in concave n person games"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "cycles in adversarial regularized learning"
    - "improved training of wasserstein gans"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-2 (induced by source paper "on nonconvex optimization for machine learning gradients stochasticity and saddle points"):
  with members:
    - "finding local minima via stochastic nested variance reduction"
    - "escaping saddles with stochastic gradients"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-3 (induced by source paper "escaping saddle points with adaptive gradient methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "the case for full matrix adaptive regularization"
    - "escaping saddle points in constrained optimization"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"

Connected through hyperedge HE-4 (induced by source paper "sgd converges to global minimum in deep learning via star convex path"):
  with members:
    - "on the convergence of adam and beyond"
    - "an alternative view when does sgd escape local minima"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "how to escape saddle points efficiently"
    - "understanding deep learning requires rethinking generalization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "finding local minima via stochastic nested variance reduction"):
  with members:
    - "stochastic variance reduced cubic regularized newton method"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "escaping saddles with stochastic gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "sub sampled cubic regularization for non convex optimization"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "escaping saddles with stochastic gradients"):
  with members:
    - "neon2 finding local minima via first order oracles"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sub sampled cubic regularization for non convex optimization"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "how to escape saddle points efficiently"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-7 (induced by source paper "on the insufficiency of existing momentum schemes for stochastic optimization"):
  with members:
    - "entropy sgd biasing gradient descent into wide valleys"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "path sgd path normalized optimization in deep neural networks"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "escaping saddle points in constrained optimization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

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)