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: "learning logistic circuits"
  Abstract: This paper proposes a new classification model called logistic circuits. On MNIST and Fashion datasets, our learning algorithm outperforms neural networks that have an order of magnitude more parameters. Yet, logistic circuits have a distinct origin in symbolic AI, forming a discriminative counterpart to probabilistic-logical circuits such as ACs, SPNs, and PSDDs. We show that parameter learning for logistic circuits is convex optimization, and that a simple local search algorithm can induce strong model structures from data.

Connected through hyperedge HE-1 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "on constrained open world probabilistic databases"
    - "on relaxing determinism in arithmetic circuits"
    - "imposing hard constraints on deep networks promises and limitations"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "equality of opportunity in supervised learning"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "embedding symbolic knowledge into deep networks"):
  with members:
    - "hyperprior induced unsupervised disentanglement of latent representations"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-3 (induced by source paper "logical interpretations of autoencoders"):
  with members:
    - "deepproblog neural probabilistic logic programming"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "emnist an extension of mnist to handwritten letters"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "programming with a differentiable forth interpreter"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-4 (induced by source paper "the convolutional tsetlin machine"):
  with members:
    - "a scheme for continuous input to the tsetlin machine with applications to forecasting disease outbreaks"
    - "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"
    - "using the tsetlin machine to learn human interpretable rules for high accuracy text categorization with medical applications"
    - "deep learning for classical japanese literature"
    - "the tsetlin machine a game theoretic bandit driven approach to optimal pattern recognition with propositional logic"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "tune a research platform for distributed model selection and training"
    - "bnn improved binary network training"

Connected through hyperedge HE-5 (induced by source paper "conditional sum product networks imposing structure on deep probabilistic architectures"):
  with members:
    - "visualizing and understanding sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "on the relative expressiveness of bayesian and neural networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "sum product quotient networks"
    - "on the latent variable interpretation in sum product networks"

Connected through hyperedge HE-6 (induced by source paper "on tractable computation of expected predictions"):
  with members:
    - "visualizing and understanding sum product networks"
    - "smoothing structured decomposable circuits"
    - "what to expect of classifiers reasoning about logistic regression with missing features"
    - "metabags bagged meta decision trees for regression"
    - "explaining image classifiers by counterfactual generation"
    - "a unified approach to interpreting model predictions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "learning compact neural networks using ordinary differential equations as activation functions"):
  with members:
    - "self normalizing neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "projectionnet learning efficient on device deep networks using neural projections"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "what to expect of classifiers reasoning about logistic regression with missing features"):
  with members:
    - "missing value imputation based on deep generative models"
    - "explaining image classifiers by counterfactual generation"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "mida multiple imputation using denoising autoencoders"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "why should i trust you explaining the predictions of any classifier"

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)