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 arithmetic circuits"
  Abstract: Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, because it requires approximate inference. We propose an alternative: learning models with a score function that directly penalizes the cost of inference. Specifically, we learn arithmetic circuits with a penalty on the number of edges in the circuit (in which the cost of inference is linear). Our algorithm is equivalent to learning a Bayesian network with context-specific independence by greedily splitting conditional distributions, at each step scoring the candidates by compiling the resulting network into an arithmetic circuit, and using its size as the penalty. We show how this can be done efficiently, without compiling a circuit from scratch for each candidate. Experiments on several real-world domains show that our algorithm is able to learn tractable models with very large treewidth, and yields more accurate predictions than a standard context-specific Bayesian network learner, in far less time.

Connected through hyperedge HE-1 (induced by source paper "learning logistic circuits"):
  with members:
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"
    - "a knowledge compilation map"

Connected through hyperedge HE-2 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"

Connected through hyperedge HE-3 (induced by source paper "maximum a posteriori inference in sum product networks"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"
    - "map complexity results and approximation methods"
    - "a knowledge compilation map"

Connected through hyperedge HE-4 (induced by source paper "on relaxing determinism in arithmetic circuits"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"
    - "a differential approach to inference in bayesian networks"
    - "algebraic model counting"
    - "sum product networks a new deep architecture"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "sum product networks for hybrid domains"):
  with members:
    - "on relaxing determinism in arithmetic circuits"
    - "on the relationship between sum product networks and bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-6 (induced by source paper "on the latent variable interpretation in sum product networks"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"
    - "a unified approach for learning the parameters of sum product networks"
    - "on the relationship between sum product networks and bayesian networks"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "sum product networks a new deep architecture"
    - "map complexity results and approximation methods"

Connected through hyperedge HE-7 (induced by source paper "expectation maximization for sum product networks as exponential family mixture models"):
  with members:
    - "on the latent variable interpretation in sum product networks"
    - "a unified approach for learning the parameters of sum product networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-8 (induced by source paper "learning arbitrary sum product network leaves with expectation maximization"):
  with members:
    - "on the latent variable interpretation in sum product networks"
    - "sum product networks a new deep architecture"

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.AI(Artificial Intelligence)