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: "neural variational inference and learning in undirected graphical models"
  Abstract: Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose black-box learning and inference algorithms for undirected models that optimize a variational approximation to the log-likelihood of the model. Central to our approach is an upper bound on the log-partition function parametrized by a function q that we express as a flexible neural network. Our bound makes it possible to track the partition function during learning, to speed-up sampling, and to train a broad class of hybrid directed/undirected models via a unified variational inference framework. We empirically demonstrate the effectiveness of our method on several popular generative modeling datasets.

Connected through hyperedge HE-1 (induced by source paper "amortized bethe free energy minimization for learning mrfs"):
  with members:
    - "a tutorial on deep latent variable models of natural language"
    - "learning approximate inference networks for structured prediction"
    - "inference in probabilistic graphical models by graph neural networks"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "perceptual losses for real time style transfer and super resolution"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial variational inference and learning in markov random fields"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "calibrating energy based generative adversarial networks"
    - "generative adversarial networks as variational training of energy based models"
    - "adversarial feature learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "deep directed generative models with energy based probability estimation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "to relieve your headache of training an mrf take advil"):
  with members:
    - "calibrating energy based generative adversarial networks"
    - "generative adversarial networks as variational training of energy based models"
    - "adversarial feature learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "deep directed generative models with energy based probability estimation"
    - "gradient estimation using stochastic computation graphs"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "learning undirected models via query training"):
  with members:
    - "to relieve your headache of training an mrf take advil"
    - "adversarial variational inference and learning in markov random fields"
    - "piecewise training for undirected 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)