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: "new advances in inference by recursive conditioning"
  Abstract: Recursive Conditioning (RC) was introduced recently as the first any-space algorithm for inference in Bayesian networks which can trade time for space by varying the size of its cache at the increment needed to store a floating point number. Under full caching, RC has an asymptotic time and space complexity which is comparable to mainstream algorithms based on variable elimination and clustering (exponential in the network treewidth and linear in its size). We show two main results about RC in this paper. First, we show that its actual space requirements under full caching are much more modest than those needed by mainstream methods and study the implications of this finding. Second, we show that RC can effectively deal with determinism in Bayesian networks by employing standard logical techniques, such as unit resolution, allowing a significant reduction in its time requirements in certain cases. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.

Connected through hyperedge HE-1 (induced by source paper "case factor diagrams for structured probabilistic modeling"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "value elimination bayesian inference via backtracking search"

Connected through hyperedge HE-2 (induced by source paper "mixtures of deterministic probabilistic networks and their and or search space"):
  with members:
    - "hybrid processing of beliefs and constraints"

Connected through hyperedge HE-3 (induced by source paper "tuffy scaling up statistical inference in markov logic networks using an rdbms"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "fast and simple relational processing of uncertain data"

Connected through hyperedge HE-4 (induced by source paper "and or multi valued decision diagrams aomdds for graphical models"):
  with members:
    - "value elimination bayesian inference via backtracking search"
    - "case factor diagrams for structured probabilistic modeling"
    - "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)