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 directed generative models with energy based probability estimation"
  Abstract: Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distribution in the inner loop of training. This can be approximately achieved by Markov chain Monte Carlo methods, but may still face a formidable obstacle that is the difficulty of mixing between modes with sharp concentrations of probability. Whereas an MCMC process is usually derived from a given energy function based on mathematical considerations and requires an arbitrarily long time to obtain good and varied samples, we propose to train a deep directed generative model (not a Markov chain) so that its sampling distribution approximately matches the energy function that is being trained. Inspired by generative adversarial networks, the proposed framework involves training of two models that represent dual views of the estimated probability distribution: the energy function (mapping an input configuration to a scalar energy value) and the generator (mapping a noise vector to a generated configuration), both represented by deep neural networks.

Connected through hyperedge HE-1 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "logan latent optimisation for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "maximum entropy generators for energy based models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "general probabilistic surface optimization and log density estimation"):
  with members:
    - "deep pdf probabilistic surface optimization and density estimation"
    - "spectral normalization for generative adversarial networks"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"
    - "mine mutual information neural estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-4 (induced by source paper "adversarial fisher vectors for unsupervised representation learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "implicit generation and generalization in energy based models"
    - "task2vec task embedding for meta learning"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unsupervised representation learning by predicting image rotations"
    - "which training methods for gans do actually converge"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (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-6 (induced by source paper "maximum entropy generators for energy based models"):
  with members:
    - "efficient gan based anomaly detection"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-7 (induced by source paper "stein bridging enabling mutual reinforcement between explicit and implicit generative models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "a variational inequality perspective on generative adversarial nets"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "learning implicit generative models by teaching explicit ones"
    - "large scale gan training for high fidelity natural image synthesis"
    - "calibrating energy based generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "model based planning with energy based models"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "exponential family estimation via adversarial dynamics embedding"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning dexterous in hand manipulation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "prediction and control with temporal segment 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)