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: "data dependent path normalization in neural networks"
  Abstract: We propose a unified framework for neural net normalization, regularization and optimization, which includes Path-SGD and Batch-Normalization and interpolates between them across two different dimensions. Through this framework we investigate issue of invariance of the optimization, data dependence and the connection with natural gradients.

Connected through hyperedge HE-1 (induced by source paper "data dependent sample complexity of deep neural networks via lipschitz augmentation"):
  with members:
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "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"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-2 (induced by source paper "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "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"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "mathematics of deep learning"
    - "globally optimal gradient descent for a convnet with gaussian inputs"

Connected through hyperedge HE-3 (induced by source paper "exploring generalization in deep learning"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "path normalized optimization of recurrent neural networks with relu activations"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "norm based capacity control in neural networks"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-4 (induced by source paper "optimization on submanifolds of convolution kernels in cnns"):
  with members:
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "all you need is a good init"
    - "trading accuracy for numerical stability orthogonalization biorthogonalization and regularization"
    - "symmetry invariant optimization in deep networks"
    - "unitary evolution recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "fast convnets using group wise brain damage"):
  with members:
    - "tensorizing neural networks"
    - "perforatedcnns acceleration through elimination of redundant convolutions"
    - "compressing neural networks with the hashing trick"
    - "deep learning with limited numerical precision"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"
    - "convolutional neural networks at constrained time cost"

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)