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: "inference in probabilistic graphical models by graph neural networks"
  Abstract: A useful computation when acting in a complex environment is to infer the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops. Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then demonstrate the efficacy of this inference approach by training GNNs on an ensemble of graphical models and showing that they substantially outperform belief propagation on loopy graphs. Our message-passing algorithms generalize out of the training set to larger graphs and graphs with different structure.

Connected through hyperedge HE-1 (induced by source paper "neural enhanced belief propagation on factor graphs"):
  with members:
    - "factor graph neural network"
    - "iterative amortized inference"
    - "self normalizing neural networks"
    - "the consciousness prior"
    - "neural message passing for quantum chemistry"
    - "recurrent inference machines for solving inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-3 (induced by source paper "lanczosnet multi scale deep graph convolutional networks"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "graph partition neural networks for semi supervised classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "junction tree variational autoencoder for molecular graph generation"
    - "situation recognition with graph neural networks"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-4 (induced by source paper "pdp a general neural framework for learning constraint satisfaction solvers"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "learning a sat solver from single bit supervision"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep sets"
    - "streamlining variational inference for constraint satisfaction problems"
    - "recurrent relational networks"

Connected through hyperedge HE-5 (induced by source paper "learning to reason leveraging neural networks for approximate dnf counting"):
  with members:
    - "learning a sat solver from single bit supervision"
    - "learning to solve np complete problems a graph neural network for decision tsp"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "how powerful are graph neural networks"
    - "on hashing based approaches to approximate dnf counting"
    - "gated graph sequence neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-6 (induced by source paper "gmnn graph markov neural networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "convolutional 2d knowledge graph embeddings"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-7 (induced by source paper "alpha belief propagation as fully factorized approximation"):
  with members:
    - "restricted boltzmann machines introduction and review"
    - "deeply learning the messages in message passing inference"
    - "optimality of large mimo detection via approximate message passing"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-8 (induced by source paper "extraction of airways using graph neural networks"):
  with members:
    - "mean field network based graph refinement with application to airway tree extraction"
    - "extraction of airways with probabilistic state space models and bayesian smoothing"
    - "gated graph sequence neural networks"
    - "mean field networks"

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)