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: "mean field networks"
  Abstract: The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting information from the neighbors. This process can be equivalently converted into a feedforward network, with each layer representing one iteration of mean field and with tied weights on all layers. This conversion enables a few natural extensions, e.g. untying the weights in the network. In this paper, we study these mean field networks (MFNs), and use them as inference tools as well as discriminative models. Preliminary experiment results show that MFNs can learn to do inference very efficiently and perform significantly better than mean field as discriminative models.

Connected through hyperedge HE-1 (induced by source paper "pixel adaptive convolutional neural networks"):
  with members:
    - "image transformer"
    - "convolutional crfs for semantic segmentation"
    - "splatnet sparse lattice networks for point cloud processing"
    - "fast end to end trainable guided filter"
    - "segmentation aware convolutional networks using local attention masks"
    - "non local neural networks"
    - "attention is all you need"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-2 (induced by source paper "continuous graph flow for flexible density estimation"):
  with members:
    - "layoutvae stochastic scene layout generation from a label set"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (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"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "inference in probabilistic graphical models by graph neural networks"):
  with members:
    - "situation recognition with graph neural networks"
    - "neural message passing for quantum chemistry"
    - "convolutional pose machines"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"
    - "discriminative embeddings of latent variable models for structured data"
    - "deeply learning the messages in message passing inference"

Connected through hyperedge HE-5 (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"
    - "inference in probabilistic graphical models by graph neural networks"
    - "extraction of airways with probabilistic state space models and bayesian smoothing"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-6 (induced by source paper "mean field network based graph refinement with application to airway tree extraction"):
  with members:
    - "extraction of airways with probabilistic state space models and bayesian smoothing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "video propagation networks"):
  with members:
    - "fast optical flow using dense inverse search"
    - "learning video object segmentation from static images"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "playing for data ground truth from computer games"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "semantic video segmentation exploring inference efficiency"

Connected through hyperedge HE-8 (induced by source paper "multi modal mean fields via cardinality based clamping"):
  with members:
    - "joint optical flow and temporally consistent semantic segmentation"
    - "principled parallel mean field inference for discrete random fields"
    - "higher order potentials in end to end trainable conditional random fields"
    - "conditional random fields as recurrent neural networks"
    - "multi scale context aggregation by dilated convolutions"
    - "clamping improves trw and mean field approximations"
    - "deepmatching hierarchical deformable dense matching"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"

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)