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 the saddle point problem for non convex optimization"
  Abstract: A central challenge to many fields of science and engineering involves minimizing non-convex error functions over continuous, high dimensional spaces. Gradient descent or quasi-Newton methods are almost ubiquitously used to perform such minimizations, and it is often thought that a main source of difficulty for the ability of these local methods to find the global minimum is the proliferation of local minima with much higher error than the global minimum. Here we argue, based on results from statistical physics, random matrix theory, and neural network theory, that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum. Motivated by these arguments, we propose a new algorithm, the saddle-free Newton method, that can rapidly escape high dimensional saddle points, unlike gradient descent and quasi-Newton methods. We apply this algorithm to deep neural network training, and provide preliminary numerical evidence for its superior performance.

Connected through hyperedge HE-1 (induced by source paper "the asymptotic spectrum of the hessian of dnn throughout training"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on exact computation with an infinitely wide neural net"
    - "gradient descent happens in a tiny subspace"
    - "a convergence theory for deep learning via over parameterization"
    - "towards understanding generalization of deep learning perspective of loss landscapes"

Connected through hyperedge HE-2 (induced by source paper "towards understanding generalization in gradient based meta learning"):
  with members:
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "three factors influencing minima in sgd"
    - "measuring and regularizing networks in function space"
    - "promp proximal meta policy search"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "overfitting mechanism and avoidance in deep neural networks"):
  with members:
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "understanding deep learning requires rethinking generalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "train faster generalize better stability of stochastic gradient descent"
    - "qualitatively characterizing neural network optimization problems"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-4 (induced by source paper "fixed horizon temporal difference methods for stable reinforcement learning"):
  with members:
    - "using a logarithmic mapping to enable lower discount factors in reinforcement learning"
    - "online off policy prediction"
    - "understanding deep learning requires rethinking generalization"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to predict independent of span"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-5 (induced by source paper "numerically recovering the critical points of a deep linear autoencoder"):
  with members:
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "sharp minima can generalize for deep nets"
    - "deep learning in neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-6 (induced by source paper "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "deep learning is robust to massive label noise"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "measuring the effects of data parallelism on neural network training"

Connected through hyperedge HE-7 (induced by source paper "gradient descent happens in a tiny subspace"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "deep residual learning for image recognition"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural tangent kernel convergence and generalization in neural networks"):
  with members:
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "the loss surfaces of multilayer networks"
    - "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)