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: "on the ability of neural nets to express distributions"
  Abstract: Deep neural nets have caused a revolution in many classification tasks. A related ongoing revolution---also theoretically not understood---concerns their ability to serve as generative models for complicated types of data such as images and texts. These models are trained using ideas like variational autoencoders and Generative Adversarial Networks. #R##N#We take a first cut at explaining the expressivity of multilayer nets by giving a sufficient criterion for a function to be approximable by a neural network with $n$ hidden layers. A key ingredient is Barron's Theorem \cite{Barron1993}, which gives a Fourier criterion for approximability of a function by a neural network with 1 hidden layer. We show that a composition of $n$ functions which satisfy certain Fourier conditions ("Barron functions") can be approximated by a $n+1$-layer neural network. #R##N#For probability distributions, this translates into a criterion for a probability distribution to be approximable in Wasserstein distance---a natural metric on probability distributions---by a neural network applied to a fixed base distribution (e.g., multivariate gaussian). #R##N#Building up recent lower bound work, we also give an example function that shows that composition of Barron functions is more expressive than Barron functions alone.

Connected through hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks"):
  with members:
    - "backward feature correction how deep learning performs deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "implicit regularization in deep matrix factorization"
    - "on the learnability of deep random networks"

Connected through hyperedge HE-2 (induced by source paper "how do infinite width bounded norm networks look in function space"):
  with members:
    - "implicit bias of gradient descent on linear convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "understanding deep learning requires rethinking generalization"
    - "norm based capacity control in neural networks"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-3 (induced by source paper "barron spaces and the compositional function spaces for neural network models"):
  with members:
    - "a priori estimates of the population risk for residual networks"
    - "exploring generalization in deep learning"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "fourier neural networks a comparative study"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "on tighter generalization bound for deep neural networks cnns resnets and beyond"):
  with members:
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "understanding generalization and optimization performance of deep cnns"
    - "on the complexity of learning neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep hyperspherical learning"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning neural models for end to end clustering"):
  with members:
    - "sampling matters in deep embedding learning"
    - "deep metric learning with angular loss"
    - "neural expectation maximization"
    - "learning to cluster in order to transfer across domains and tasks"
    - "deep metric learning via facility location"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "joint unsupervised learning of deep representations and image clusters"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-7 (induced by source paper "on the optimization of deep networks implicit acceleration by overparameterization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "depth separation for neural networks"
    - "spurious local minima are common in two layer relu neural networks"
    - "training input output recurrent neural networks through spectral methods"
    - "identity matters in deep learning"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "emergence of invariance and disentanglement in deep representations"):
  with members:
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "sharp minima can generalize for deep nets"
    - "opening the black box of deep neural networks via information"
    - "deep variational information bottleneck"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "amortised map inference for image super resolution"

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)