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: "solving sat and bayesian inference with backtracking search"
  Abstract: Inference in Bayes Nets (BAYES) is an important problem with numerous applications in probabilistic reasoning. Counting the number of satisfying assignments of a propositional formula (#SAT) is a closely related problem of fundamental theoretical importance. Both these problems, and others, are members of the class of sum-of-products (SUMPROD) problems. In this paper we show that standard backtracking search when augmented with a simple memoization scheme (caching) can solve any sum-of-products problem with time complexity that is at least as good any other state-of-the-art exact algorithm, and that it can also achieve the best known time-space tradeoff. Furthermore, backtracking's ability to utilize more flexible variable orderings allows us to prove that it can achieve an exponential speedup over other standard algorithms for SUMPROD on some instances.#R##N##R##N#The ideas presented here have been utilized in a number of solvers that have been applied to various types of sum-of-product problem's. These system's have exploited the fact that backtracking can naturally exploit more of the problem's structure to achieve improved performance on a range of problem instances. Empirical evidence of this performance gain has appeared in published works describing these solvers, and we provide references to these works.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "abstracting probabilistic models a logical perspective"):
  with members:
    - "state space abstractions for probabilistic inference a systematic review"
    - "explaining transition systems through program induction"
    - "probabilistic program abstractions"
    - "symmetric weighted first order model counting"
    - "context specific independence in bayesian networks"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "bisimulation based approximate lifted inference"
    - "markov logic in infinite domains"

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 "approximate inference in discrete distributions with monte carlo tree search and value functions"):
  with members:
    - "meta learning mcmc proposals"
    - "etalumis bringing probabilistic programming to scientific simulators at scale"
    - "relational inductive biases deep learning and graph networks"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"

Connected through hyperedge HE-6 (induced by source paper "fairness in machine learning with tractable models"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "on relaxing determinism in arithmetic circuits"
    - "equality of opportunity in supervised learning"
    - "sum product networks a new deep architecture"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "compiling stochastic constraint programs to and or decision diagrams"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "spudd stochastic planning using decision diagrams"
    - "stochastic constraint programming"
    - "and or multi valued decision diagrams aomdds for graphical models"
    - "stochastic constraint programming a scenario based approach"

Connected through hyperedge HE-8 (induced by source paper "learning tractable probabilistic models in open worlds"):
  with members:
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "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"

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)