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 expressive efficiency of sum product networks"
  Abstract: Sum Product Networks (SPNs) are a recently developed class of deep generative models which compute their associated unnormalized density functions using a special type of arithmetic circuit. When certain sufficient conditions, called the decomposability and completeness conditions (or "D&C" conditions), are imposed on the structure of these circuits, marginal densities and other useful quantities, which are typically intractable for other deep generative models, can be computed by what amounts to a single evaluation of the network (which is a property known as "validity"). However, the effect that the D&C conditions have on the capabilities of D&C SPNs is not well understood. #R##N#In this work we analyze the D&C conditions, expose the various connections that D&C SPNs have with multilinear arithmetic circuits, and consider the question of how well they can capture various distributions as a function of their size and depth. Among our various contributions is a result which establishes the existence of a relatively simple distribution with fully tractable marginal densities which cannot be efficiently captured by D&C SPNs of any depth, but which can be efficiently captured by various other deep generative models. We also show that with each additional layer of depth permitted, the set of distributions which can be efficiently captured by D&C SPNs grows in size. This kind of "depth hierarchy" property has been widely conjectured to hold for various deep models, but has never been proven for any of them. Some of our other contributions include a new characterization of the D&C conditions as sufficient and necessary ones for a slightly strengthened notion of validity, and various state-machine characterizations of the types of computations that can be performed efficiently by D&C SPNs.

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 "visualizing and understanding sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "learning tractable probabilistic models for fault localization"
    - "understanding neural networks through deep visualization"
    - "made masked autoencoder for distribution estimation"
    - "on the relationship between sum product networks and bayesian networks"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "on the sample complexity of learning sum product networks"):
  with members:
    - "bayesian learning of sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "sample efficient learning of mixtures"
    - "efficient robust proper learning of log concave distributions"
    - "expectation maximization for sum product networks as exponential family mixture models"
    - "a unified approach for learning the parameters of sum product networks"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-4 (induced by source paper "depth separations in neural networks what is actually being separated"):
  with members:
    - "provable limitations of deep learning"
    - "on the complexity of learning neural networks"
    - "failures of gradient based deep learning"
    - "depth separation for neural networks"
    - "error bounds for approximations with deep relu networks"
    - "distribution specific hardness of learning neural networks"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"

Connected through hyperedge HE-5 (induced by source paper "depth width trade offs for relu networks via sharkovsky s theorem"):
  with members:
    - "the route to chaos in routing games population increase drives period doubling instability chaos inefficiency with price of anarchy equal to one"
    - "on the expressive power of deep polynomial neural networks"
    - "is deeper better only when shallow is good"
    - "benefits of depth in neural networks"
    - "why deep neural networks for function approximation"
    - "understanding deep neural networks with rectified linear units"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-6 (induced by source paper "tucker decomposition network expressive power and comparison"):
  with members:
    - "expressive power of recurrent neural networks"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "benefits of depth in neural networks"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the expressive power of deep learning a tensor analysis"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "on the expressive power of deep polynomial neural networks"):
  with members:
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "on the expressive power of deep learning a tensor analysis"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-8 (induced by source paper "efficient deep learning of gmms"):
  with members:
    - "provable limitations of deep learning"
    - "the power of deeper networks for expressing natural functions"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "on the expressive power of deep learning a tensor analysis"

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)