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: "a new class of upper bounds on the log partition function"
  Abstract: Bounds on the log partition function are important in a variety of contexts, including approximate inference, model fitting, decision theory, and large deviations analysis. We introduce a new class of upper bounds on the log partition function, based on convex combinations of distributions in the exponential domain, that is applicable to an arbitrary undirected graphical model. In the special case of convex combinations of tree-structured distributions, we obtain a family of variational problems, similar to the Bethe free energy, but distinguished by the following desirable properties: i. they are cnvex, and have a unique global minimum; and ii. the global minimum gives an upper bound on the log partition function. The global minimum is defined by stationary conditions very similar to those defining fixed points of belief propagation or tree-based reparameterization Wainwright et al., 2001. As with BP fixed points, the elements of the minimizing argument can be used as approximations to the marginals of the original model. The analysis described here can be extended to structures of higher treewidth e.g., hypertrees, thereby making connections with more advanced approximations e.g., Kikuchi and variants Yedidia et al., 2001; Minka, 2001.

Connected through hyperedge HE-1 (induced by source paper "visual semantic information pursuit a survey"):
  with members:
    - "linknet relational embedding for scene graph"
    - "iterative visual reasoning beyond convolutions"
    - "graph r cnn for scene graph generation"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "large scale visual relationship understanding"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"

Connected through hyperedge HE-2 (induced by source paper "graph structured prediction energy networks"):
  with members:
    - "deep structured prediction with nonlinear output transformations"
    - "learning approximate inference networks for structured prediction"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-3 (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"
    - "neural variational inference and learning in undirected graphical 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"

Connected through hyperedge HE-4 (induced by source paper "end to end training of cnn crf via differentiable dual decomposition"):
  with members:
    - "ccnet criss cross attention for semantic segmentation"
    - "end to end learning for graph decomposition"
    - "non local neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep learning markov random field for semantic segmentation"
    - "end to end training of hybrid cnn crf models for stereo"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "neural variational inference and learning in undirected graphical 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"

Connected through hyperedge HE-6 (induced by source paper "learning correlated latent representations with adaptive priors"):
  with members:
    - "correlated variational auto encoders"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "tensorflow a system for large scale machine learning"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-7 (induced by source paper "correlated variational auto encoders"):
  with members:
    - "interpretable vaes for nonlinear group factor analysis"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "importance weighted autoencoders"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "spectral approximate inference"):
  with members:
    - "the mean field approximation information inequalities algorithms and complexity"
    - "the ising partition function zeros and deterministic approximation"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "variable elimination in the fourier domain"
    - "inapproximability for antiferromagnetic spin systems in the tree non uniqueness region"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"

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)