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: "adaptive importance sampling for estimation in structured domains"
  Abstract: Sampling is an important tool for estimating large, complex sums and integrals over high dimensional spaces. For instance, important sampling has been used as an alternative to exact methods for inference in belief networks. Ideally, we want to have a sampling distribution that provides optimal-variance estimators. In this paper, we present methods that improve the sampling distribution by systematically adapting it as we obtain information from the samples. We present a stochastic-gradient-descent method for sequentially updating the sampling distribution based on the direct minization of the variance. We also present other stochastic-gradient-descent methods based on the minimizationof typical notions of distance between the current sampling distribution and approximations of the target, optimal distribution. We finally validate and compare the different methods empirically by applying them to the problem of action evaluation in influence diagrams.

Connected through hyperedge HE-1 (induced by source paper "correlated equilibria for approximate variational inference in mrfs"):
  with members:
    - "graphical potential games"
    - "on sparse discretization for graphical games"
    - "projecting ising model parameters for fast mixing"
    - "graphical models for game theory"
    - "a new class of upper bounds on the log partition function"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "settling the complexity of computing two player nash equilibria"
    - "computing nash equilibria approximation and smoothed complexity"

Connected through hyperedge HE-2 (induced by source paper "importance sampling in bayesian networks an influence based approximation strategy for importance functions"):
  with members:
    - "knowledge engineering for large belief networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "bounds on sample size for policy evaluation in markov environments"):
  with members:
    - "learning to cooperate via policy search"
    - "learning finite state controllers for partially observable environments"
    - "learning policies with external memory"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "computational investigation of low discrepancy sequences in simulation algorithms for bayesian networks"
    - "backward simulation in bayesian networks"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"

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)