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: "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
  Abstract: We propose a block-diagonal approximation of the positive-curvature Hessian (BDA-PCH) matrix to measure curvature. Our proposed BDAPCH matrix is memory efficient and can be applied to any fully-connected neural networks where the activation and criterion functions are twice differentiable. Particularly, our BDA-PCH matrix can handle non-convex criterion functions. We devise an efficient scheme utilizing the conjugate gradient method to derive Newton directions for mini-batch setting. Empirical studies show that our method outperforms the competing second-order methods in convergence speed.

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"
    - "know what you don t know unanswerable questions for squad"
    - "block diagonal hessian free optimization for training neural networks"
    - "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 "modular block diagonal curvature approximations for feedforward architectures"):
  with members:
    - "deepobs a deep learning optimizer benchmark suite"
    - "the outer product structure of neural network derivatives"
    - "feedforward and recurrent neural networks backward propagation and hessian in matrix form"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"

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)