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: "tractable querying and learning in hybrid domains via sum product networks"
  Abstract: Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size. The leaf nodes in SPNs, from which more intricate mixtures are formed, are tractable univariate distributions, and so the literature has focused on Bernoulli and Gaussian random variables. This is clearly a restriction for handling mixed discrete-continuous data, especially if the continuous features are generated from non-parametric and non-Gaussian distribution families. In this work, we present a framework that systematically integrates SPN structure learning with weighted model integration, a recently introduced computational abstraction for performing inference in hybrid domains, by means of piecewise polynomial approximations of density functions of arbitrary shape. Our framework is instantiated by exploiting the notion of propositional abstractions, thus minimally interfering with the SPN structure learning module, and supports a powerful query interface for conditioning on interval constraints. Our empirical results show that our approach is effective, and allows a study of the trade off between the granularity of the learned model and its predictive power.

Connected through hyperedge HE-1 (induced by source paper "smt ilp"):
  with members:
    - "approximate counting in smt and value estimation for probabilistic programs"

Connected through hyperedge HE-2 (induced by source paper "progressive stochastic binarization of deep networks"):
  with members:
    - "deep convolutional sum product networks for probabilistic image representations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "learning to train a binary neural network"
    - "bnn improved binary network training"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "on the sample complexity of learning sum product networks"):
  with members:
    - "bayesian learning of sum product networks"
    - "learning deep mixtures of gaussian process experts using sum product networks"
    - "sample efficient learning of mixtures"
    - "efficient robust proper learning of log concave distributions"
    - "expectation maximization for sum product networks as exponential family mixture models"
    - "a unified approach for learning the parameters of sum product networks"
    - "on the expressive efficiency of sum product networks"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"

Connected through hyperedge HE-4 (induced by source paper "fairness in machine learning with tractable models"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "equality of opportunity in supervised learning"
    - "sum product networks a new deep architecture"
    - "fairness through awareness"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-5 (induced by source paper "learning tractable probabilistic models in open worlds"):
  with members:
    - "incremental knowledge base construction using deepdive"
    - "a differential approach to inference in bayesian networks"
    - "programming with personalized pagerank a locally groundable first order probabilistic logic"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"

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)