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: "on relaxing determinism in arithmetic circuits"
  Abstract: The past decade has seen a significant interest in learning tractable probabilistic representations. Arithmetic circuits (ACs) were among the first proposed tractable representations, with some subsequent representations being instances of ACs with weaker or stronger properties. In this paper, we provide a formal basis under which variants on ACs can be compared, and where the precise roles and semantics of their various properties can be made more transparent. This allows us to place some recent developments on ACs in a clearer perspective and to also derive new results for ACs. This includes an exponential separation between ACs with and without determinism; completeness and incompleteness results; and tractability results (or lack thereof) when computing most probable explanations (MPEs).

Connected through hyperedge HE-1 (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"
    - "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"
    - "a differential approach to inference in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "on constraint definability in tractable probabilistic models"):
  with members:
    - "on constrained open world probabilistic databases"
    - "learning logistic 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-3 (induced by source paper "an advance on variable elimination with applications to tensor based computation"):
  with members:
    - "efficient contraction of large tensor networks for weighted model counting through graph decompositions"
    - "on the relative expressiveness of bayesian and neural networks"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "a differential approach to inference in bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (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 the relationship between sum product networks and bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-5 (induced by source paper "visualizing and understanding sum product networks"):
  with members:
    - "approximation complexity of maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "learning tractable probabilistic models for fault localization"
    - "understanding neural networks through deep visualization"
    - "made masked autoencoder for distribution estimation"
    - "on the relationship between sum product networks and bayesian networks"
    - "visualizing and understanding recurrent networks"
    - "on the expressive efficiency of sum product networks"

Connected through hyperedge HE-6 (induced by source paper "conditional sum product networks imposing structure on deep probabilistic architectures"):
  with members:
    - "visualizing and understanding sum product networks"
    - "learning logistic circuits"
    - "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"
    - "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-7 (induced by source paper "smoothing structured decomposable circuits"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "maximum a posteriori inference in sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "the sum product theorem a foundation for learning tractable models"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "expander cnfs have exponential dnnf size"
    - "skolemization for weighted first order model counting"

Connected through hyperedge HE-8 (induced by source paper "fairness in machine learning with tractable models"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "equality of opportunity in supervised learning"
    - "sum product networks a new deep architecture"
    - "fairness through awareness"
    - "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.AI(Artificial Intelligence)