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: "case factor diagrams for structured probabilistic modeling"
  Abstract: We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Boolean formulas that we call case-factor diagrams (CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for circuits of bounded tree width (unlike BDDs) and can concisely represent the set of parse trees over a given string undera given context free grammar (also unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of Boolean assignments and a weight (or cost) for each individual Boolean variable. We give an insideoutside algorithm for simultaneously computing the marginal of each Boolean variable, and a Viterbi algorithm for finding the mininum cost variable assignment. Both algorithms run in time proportional to the size of the CFD.

Connected through hyperedge HE-1 (induced by source paper "an algebraic formalization of forward and forward backward algorithms"):
  with members:
    - "the sum product theorem a foundation for learning tractable models"

Connected through hyperedge HE-2 (induced by source paper "monolingual probabilistic programming using generalized coroutines"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "church a language for generative models"

Connected through hyperedge HE-3 (induced by source paper "sum product networks a new deep architecture"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "learning arithmetic circuits"

Connected through hyperedge HE-4 (induced by source paper "propagation using chain event graphs"):
  with members:
    - "context specific independence in bayesian networks"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "and or multi valued decision diagrams aomdds for weighted graphical models"):
  with members:
    - "a knowledge compilation map"

Connected through hyperedge HE-6 (induced by source paper "search based structured prediction"):
  with members:
    - "pegasus a policy search method for large mdps and pomdps"
    - "bayesian query focused summarization"
    - "a noisy channel model for document compression"
    - "a large scale exploration of effective global features for a joint entity detection and tracking model"
    - "learning as search optimization approximate large margin methods for structured prediction"
    - "the use of classifiers in sequential inference"

Connected through hyperedge HE-7 (induced by source paper "learning as search optimization approximate large margin methods for structured prediction"):
  with members:
    - "the use of classifiers in sequential inference"

Connected through hyperedge HE-8 (induced by source paper "and or multi valued decision diagrams aomdds for graphical models"):
  with members:
    - "value elimination bayesian inference via backtracking search"
    - "new advances in inference by recursive conditioning"
    - "mixtures of deterministic probabilistic networks and their and or search space"
    - "the relationship between and or search and variable elimination"
    - "and or multi valued decision diagrams aomdds for weighted graphical models"
    - "a knowledge compilation map"

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)