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: "effect of depth and width on local minima in deep learning"
  Abstract: In this paper, we analyze the effects of depth and width on the quality of local minima, without strong overparameterization and simplification assumptions in the literature. Without any simplifica...

Connected through hyperedge HE-1 (induced by source paper "duality of width and depth of neural networks"):
  with members:
    - "linear algebra and duality of neural networks"
    - "model compression via distillation and quantization"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of quadratic networks"
    - "the power of deeper networks for expressing natural functions"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "why deep neural networks for function approximation"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "large scale representation learning from triplet comparisons"):
  with members:
    - "estimation of perceptual scales using ordinal embedding"
    - "a theoretical analysis of contrastive unsupervised representation learning"
    - "an efficient framework for learning sentence representations"
    - "deep metric learning with hierarchical triplet loss"
    - "the loss surface of deep and wide neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "learning combinatorial functions from pairwise comparisons"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "elimination of all bad local minima in deep learning"):
  with members:
    - "deep learning for classical japanese literature"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "deep neural networks with multi branch architectures are less non convex"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "are resnets provably better than linear predictors"
    - "learning one hidden layer neural networks with landscape design"

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)