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: "deep mixtures of factor analysers"
  Abstract: An efficient way to learn deep density models that have many layers of latent variables is to learn one layer at a time using a model that has only one layer of latent variables. After learning each layer, samples from the posterior distributions for that layer are used as training data for learning the next layer. This approach is commonly used with Restricted Boltzmann Machines, which are undirected graphical models with a single hidden layer, but it can also be used with Mixtures of Factor Analysers (MFAs) which are directed graphical models. In this paper, we present a greedy layer-wise learning algorithm for Deep Mixtures of Factor Analysers (DMFAs). Even though a DMFA can be converted to an equivalent shallow MFA by multiplying together the factor loading matrices at different levels, learning and inference are much more efficient in a DMFA and the sharing of each lower-level factor loading matrix by many different higher level MFAs prevents overfitting. We demonstrate empirically that DMFAs learn better density models than both MFAs and two types of Restricted Boltzmann Machine on a wide variety of datasets.

Connected through hyperedge HE-1 (induced by source paper "a probabilistic representation of deep learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "exploring generalization in deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"
    - "nearly tight vc dimension and pseudodimension bounds for piecewise linear neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "explicitly bayesian regularizations in deep learning"):
  with members:
    - "implicit regularization in deep matrix factorization"
    - "a synthetic dataset for deep learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "exploring generalization in deep learning"
    - "sharp minima can generalize for deep nets"
    - "understanding deep learning requires rethinking generalization"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a provably correct algorithm for deep learning that actually works"):
  with members:
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "sgd learns the conjugate kernel class of the network"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "when is a convolutional filter easy to learn"
    - "deep learning and hierarchal generative models"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "neural autoregressive distribution estimation"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "pixel recurrent neural networks"
    - "importance weighted autoencoders"
    - "made masked autoencoder for distribution estimation"
    - "generative moment matching networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "nice non linear independent components estimation"):
  with members:
    - "how auto encoders could provide credit assignment in deep networks via target propagation"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "learning the irreducible representations of commutative lie groups"
    - "deep autoregressive networks"
    - "better mixing via deep representations"
    - "theano new features and speed improvements"

Connected through hyperedge HE-6 (induced by source paper "learning generative models with visual attention"):
  with members:
    - "on learning where to look"
    - "generalized denoising auto encoders as generative models"
    - "learning where to attend with deep architectures for image tracking"

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)