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: "operations for learning with graphical models"
  Abstract: This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-2 (induced by source paper "from high level inference algorithms to efficient code"):
  with members:
    - "autoconj recognizing and exploiting conjugacy without a domain specific language"
    - "exact inference for relational graphical models with interpreted functions lifted probabilistic inference modulo theories"
    - "swift compiled inference for probabilistic programming languages"
    - "probabilistic inference modulo theories"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "dynamic blocking and collapsing for gibbs sampling"
    - "church a language for generative models"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "amidst a java toolbox for scalable probabilistic machine learning"):
  with members:
    - "probabilistic graphical models on multi core cpus using java 8"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-4 (induced by source paper "learning probabilistic programs using backpropagation"):
  with members:
    - "inference compilation and universal probabilistic programming"
    - "deep generative stochastic networks trainable by backprop"

Connected through hyperedge HE-5 (induced by source paper "the complexity of bayesian networks specified by propositional and relational languages"):
  with members:
    - "on the semantics and complexity of probabilistic logic programs"
    - "new liftable classes for first order probabilistic inference"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "lower complexity bounds for lifted inference"
    - "tractability through exchangeability a new perspective on efficient probabilistic inference"
    - "understanding the complexity of lifted inference and asymmetric weighted model counting"
    - "query dags a practical paradigm for implementing belief network inference"
    - "a simple fptas for counting edge covers"

Connected through hyperedge HE-6 (induced by source paper "modelwizard toward interactive model construction"):
  with members:
    - "associative arrays unified mathematics for spreadsheets databases matrices and graphs"
    - "the sql query language configurable unifying and semi structured"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "probabilistic programming concepts"
    - "compiling relational database schemata into probabilistic graphical models"

Connected through hyperedge HE-7 (induced by source paper "cooperative joint localization and clock synchronization based on gaussian message passing in asynchronous wireless networks"):
  with members:
    - "cooperative synchronization in wireless networks"
    - "collaborative high accuracy localization in mobile multipath environments"
    - "merging belief propagation and the mean field approximation a free energy approach"

Connected through hyperedge HE-8 (induced by source paper "a survey on domain specific languages for machine learning in big data"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graphlab a new framework for parallel machine learning"

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)