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: "sum product networks a new deep architecture"
  Abstract: The key limiting factor in graphical model inference and learning is the complexity of the partition function. We thus ask the question: what are general conditions under which the partition function is tractable? The answer leads to a new kind of deep architecture, which we call sum-product networks (SPNs). SPNs are directed acyclic graphs with variables as leaves, sums and products as internal nodes, and weighted edges. We show that if an SPN is complete and consistent it represents the partition function and all marginals of some graphical model, and give semantics to its nodes. Essentially all tractable graphical models can be cast as SPNs, but SPNs are also strictly more general. We then propose learning algorithms for SPNs, based on backpropagation and EM. Experiments show that inference and learning with SPNs can be both faster and more accurate than with standard deep networks. For example, SPNs perform image completion better than state-of-the-art deep networks for this task. SPNs also have intriguing potential connections to the architecture of the cortex.

Connected through hyperedge HE-1 (induced by source paper "training deep energy based models with f divergence minimization"):
  with members:
    - "exponential family estimation via adversarial dynamics embedding"
    - "maximum entropy generators for energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-2 (induced by source paper "semiring programming a declarative framework for generalized sum product problems"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "recursive decomposition for nonconvex optimization"
    - "the sum product theorem a foundation for learning tractable models"
    - "stable model counting and its application in probabilistic logic programming"
    - "optimization modulo theories with linear rational costs"
    - "on the complexity of optimization problems based on compiled nnf representations"
    - "approximate counting in smt and value estimation for probabilistic programs"
    - "algebraic model counting"

Connected through hyperedge HE-3 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations"):
  with members:
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "counterfactuals uncover the modular structure of deep generative models"
    - "on relaxing determinism in arithmetic circuits"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "budgeted experiment design for causal structure learning"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "on constrained open world probabilistic databases"
    - "learning logistic circuits"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "cryptospn privacy preserving sum product network inference"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "sgxpectre stealing intel secrets from sgx enclaves via speculative execution"
    - "gazelle a low latency framework for secure neural network inference"
    - "on relaxing determinism in arithmetic circuits"
    - "on the relationship between sum product networks and bayesian networks"

Connected through hyperedge HE-6 (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"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "learning to train a binary neural network"
    - "bnn improved binary network training"

Connected through hyperedge HE-7 (induced by source paper "learning models over relational data a brief tutorial"):
  with members:
    - "a layered aggregate engine for analytics workloads"
    - "incremental techniques for large scale dynamic query processing"
    - "counting triangles under updates in worst case optimal time"
    - "on functional aggregate queries with additive inequalities"
    - "size bounds and query plans for relational joins"
    - "incremental view maintenance with triple lock factorization benefits"
    - "constraint solving via fractional edge covers"
    - "boolean tensor decomposition for conjunctive queries with negation"

Connected through hyperedge HE-8 (induced by source paper "logical interpretations of autoencoders"):
  with members:
    - "learning logistic circuits"
    - "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"

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)