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: "estimating the hessian by back propagating curvature"
  Abstract: In this work we develop Curvature Propagation (CP), a general technique for efficiently computing unbiased approximations of the Hessian of any function that is computed using a computational graph. At the cost of roughly two gradient evaluations, CP can give a rank-1 approximation of the whole Hessian, and can be repeatedly applied to give increasingly precise unbiased estimates of any or all of the entries of the Hessian. Of particular interest is the diagonal of the Hessian, for which no general approach is known to exist that is both efficient and accurate. We show in experiments that CP turns out to work well in practice, giving very accurate estimates of the Hessian of neural networks, for example, with a relatively small amount of work. We also apply CP to Score Matching, where a diagonal of a Hessian plays an integral role in the Score Matching objective, and where it is usually computed exactly using inefficient algorithms which do not scale to larger and more complex models.

Connected through hyperedge HE-1 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "proximal backpropagation"

Connected through hyperedge HE-2 (induced by source paper "differentiate everything with a reversible programming language"):
  with members:
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "effective extensible programming unleashing julia on gpus"
    - "don t unroll adjoint differentiating ssa form programs"
    - "invertible residual networks"
    - "fashionable modelling with flux"
    - "auto differentiating linear algebra"
    - "design and implementation of a reversible object oriented programming language"
    - "density estimation using real nvp"

Connected through hyperedge HE-3 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-4 (induced by source paper "better pac bayes bounds for deep neural networks using the loss curvature"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "limitations of the empirical fisher approximation"
    - "information matrices and generalization"
    - "the information complexity of learning tasks their structure and their distance"
    - "some limitations of norm based generalization bounds in deep neural networks"
    - "data dependent pac bayes priors via differential privacy"
    - "fixing variational bayes deterministic variational inference for bayesian neural networks"

Connected through hyperedge HE-5 (induced by source paper "neural networks with cheap differential operators"):
  with members:
    - "neural autoregressive flows"
    - "invertible residual networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "density estimation using real nvp"
    - "accelerating eulerian fluid simulation with convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "sliced score matching a scalable approach to density and score estimation"):
  with members:
    - "functional variational bayesian neural networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep learning face attributes in the wild"
    - "bregman divergence as general framework to estimate unnormalized statistical models"

Connected through hyperedge HE-7 (induced by source paper "improving dnn robustness to adversarial attacks using jacobian regularization"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "classification regions of deep neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "gradient regularization improves accuracy of discriminative models"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "analysis of universal adversarial perturbations"

Connected through hyperedge HE-8 (induced by source paper "rate distortion for model compression from theory to practice"):
  with members:
    - "amc automl for model compression and acceleration on mobile devices"
    - "learning one hidden layer neural networks with landscape design"
    - "on calibration of modern neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards the limit of network quantization"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

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)