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: "minimum probability flow learning"
  Abstract: Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function and its derivatives. Here we propose a new parameter estimation technique that does not require computing an intractable normalization factor or sampling from the equilibrium distribution of the model. This is achieved by establishing dynamics that would transform the observed data distribution into the model distribution, and then setting as the objective the minimization of the KL divergence between the data distribution and the distribution produced by running the dynamics for an infinitesimal time. Score matching, minimum velocity learning, and certain forms of contrastive divergence are shown to be special cases of this learning technique. We demonstrate parameter estimation in Ising models, deep belief networks and an independent component analysis model of natural scenes. In the Ising model case, current state of the art techniques are outperformed by at least an order of magnitude in learning time, with lower error in recovered coupling parameters.

Connected through hyperedge HE-1 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "sliced score matching a scalable approach to density and score estimation"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "energy inspired models learning with sampler induced distributions"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "divide and couple using monte carlo variational objectives for posterior approximation"
    - "importance weighting and variational inference"
    - "kernel exponential family estimation via doubly dual embedding"
    - "noise contrastive estimation and negative sampling for conditional models consistency and statistical efficiency"
    - "hamiltonian variational auto encoder"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-3 (induced by source paper "exponential family estimation via adversarial dynamics embedding"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "hamiltonian variational auto encoder"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "on better training the infinite restricted boltzmann machines"):
  with members:
    - "learning infinite rbms with frank wolfe"
    - "an infinite restricted boltzmann machine"

Connected through hyperedge HE-5 (induced by source paper "variational probability flow for biologically plausible training of deep neural networks"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "decoupled neural interfaces using synthetic gradients"
    - "theano a python framework for fast computation of mathematical expressions"
    - "towards biologically plausible deep learning"
    - "how important is weight symmetry in backpropagation"
    - "adam a method for stochastic optimization"
    - "deep generative stochastic networks trainable by backprop"
    - "better mixing via deep representations"

Connected through hyperedge HE-6 (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-7 (induced by source paper "deep unsupervised learning using nonequilibrium thermodynamics"):
  with members:
    - "nice non linear independent components estimation"
    - "reweighted wake sleep"
    - "accurate and conservative estimates of mrf log likelihood using reverse annealing"
    - "loopy belief propagation for approximate inference an empirical study"
    - "deep generative stochastic networks trainable by backprop"
    - "fast large scale optimization by unifying stochastic gradient and quasi newton methods"
    - "deep autoregressive networks"
    - "better mixing via deep representations"

Connected through hyperedge HE-8 (induced by source paper "a deep embedding model for co occurrence learning"):
  with members:
    - "generalized denoising auto encoders as generative models"

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)