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: "neon2 finding local minima via first order oracles"
  Abstract: We propose a reduction for non-convex optimization that can (1) turn a stationary-point finding algorithm into a local-minimum finding one, and (2) replace the Hessian-vector product computations with only gradient computations. It works both in the stochastic and the deterministic settings, without hurting the algorithm's performance. #R##N#As applications, our reduction turns Natasha2 into a first-order method without hurting its performance. It also converts SGD, GD, SCSG, and SVRG into local-minimum finding algorithms outperforming some best known results.

Connected through hyperedge HE-1 (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"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-2 (induced by source paper "stabilized svrg simple variance reduction for nonconvex optimization"):
  with members:
    - "stochastic nested variance reduction for nonconvex optimization"
    - "finding local minima via stochastic nested variance reduction"
    - "learning one hidden layer neural networks with landscape design"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-3 (induced by source paper "perturbed proximal descent to escape saddle points for non convex and non smooth objective functions"):
  with members:
    - "how to escape saddle points efficiently"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-4 (induced by source paper "a convergence theory for deep learning via over parameterization"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "learning compact neural networks with regularization"
    - "on the convergence rate of training recurrent neural networks"
    - "learning long term dependencies via fourier recurrent units"

Connected through hyperedge HE-5 (induced by source paper "defending against saddle point attack in byzantine robust distributed learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "high dimensional robust sparse regression"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-6 (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"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a generic approach for escaping saddle points"
    - "sub sampled cubic regularization for non convex optimization"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-7 (induced by source paper "escaping saddles with stochastic gradients"):
  with members:
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "a generic approach for escaping saddle points"
    - "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-8 (induced by source paper "local saddle point optimization a curvature exploitation approach"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "the numerics of gans"
    - "gradient descent gan optimization is locally stable"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

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)