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: "block diagonal hessian free optimization for training neural networks"
  Abstract: Second-order methods for neural network optimization have several advantages over methods based on first-order gradient descent, including better scaling to large mini-batch sizes and fewer updates needed for convergence. But they are rarely applied to deep learning in practice because of high computational cost and the need for model-dependent algorithmic variations. We introduce a vari- ant of the Hessian-free method that leverages a block-diagonal approximation of the generalized Gauss-Newton matrix. Our method computes the curvature approximation matrix only for pairs of parameters from the same layer or block of the neural network and performs conjugate gradient updates independently for each block. Experiments on deep autoencoders, deep convolutional networks, and multilayer LSTMs demonstrate better convergence and generalization compared to the original Hessian-free approach and the Adam method.

Connected through hyperedge HE-1 (induced by source paper "deepobs a deep learning optimizer benchmark suite"):
  with members:
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
    - "know what you don t know unanswerable questions for squad"
    - "tbd benchmarking and analyzing deep neural network training"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "small steps and giant leaps minimal newton solvers for deep learning"):
  with members:
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "adam a method for stochastic optimization"
    - "return of the devil in the details delving deep into convolutional nets"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "new insights and perspectives on the natural gradient method"
    - "revisiting natural gradient for deep networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "bda pch block diagonal approximation of positive curvature hessian for training neural networks"):
  with members:
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "robust optimization for deep regression"
    - "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)