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: "mixtures of deterministic probabilistic networks and their and or search space"
  Abstract: The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with constraint networks, defining the semantics and graphical representation. We also introduce the AND/OR search space for graphical models, and develop a new linear space search algorithm. This provides the basis for understanding the benefits of processing the constraint information separately, resulting in the pruning of the search space. When the constraint part is tractable or has a small number of solutions, using the mixed representation can be exponentially more effective than using pure belief networks which odel constraints as conditional probability tables.

Connected through hyperedge HE-1 (induced by source paper "world knowledge as indirect supervision for document clustering"):
  with members:
    - "predicting social links for new users across aligned heterogeneous social networks"
    - "lambda dependency based compositional semantics"
    - "selective transfer learning for cross domain recommendation"
    - "learning dependency based compositional semantics"
    - "wikipedia based semantic interpretation for natural language processing"

Connected through hyperedge HE-2 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "hybrid processing of beliefs and constraints"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "a simple insight into iterative belief propagation s success"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "approximate inference algorithms for hybrid bayesian networks with discrete constraints"):
  with members:
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "iterative join graph propagation"
    - "on finding minimal w cutset"
    - "an empirical study of w cutset sampling for bayesian networks"
    - "a simple insight into iterative belief propagation s success"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-4 (induced by source paper "measuring the hardness of stochastic sampling on bayesian networks with deterministic causalities the k test"):
  with members:
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "an importance sampling algorithm based on evidence pre propagation"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "studies in lower bounding probabilities of evidence using the markov inequality"):
  with members:
    - "knowledge engineering for large belief networks"
    - "approximate inference algorithms for hybrid bayesian networks with discrete constraints"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "the relationship between and or search and variable elimination"):
  with members:
    - "value elimination bayesian inference via backtracking search"

Connected through hyperedge HE-7 (induced by source paper "solving sat and bayesian inference with backtracking search"):
  with members:
    - "context specific independence in bayesian networks"
    - "and or multi valued decision diagrams aomdds for weighted graphical models"
    - "the good old davis putnam procedure helps counting models"

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"
    - "case factor diagrams for structured probabilistic modeling"
    - "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)