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: "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"
  Abstract: Stochastic sampling algorithms, while an attractive alternative to exact algorithms in very large Bayesian network models, have been observed to perform poorly in evidential reasoning with extremely unlikely evidence. To address this problem, we propose an adaptive importance sampling algorithm, AIS-BN, that shows promising convergence rates even under extreme conditions and seems to outperform the existing sampling algorithms consistently. Three sources of this performance improvement are (1) two heuristics for initialization of the importance function that are based on the theoretical properties of importance sampling in finite-dimensional integrals and the structural advantages of Bayesian networks, (2) a smooth learning method for the importance function, and (3) a dynamic weighting function for combining samples from different stages of the algorithm.#R##N##R##N#We tested the performance of the AIS-BN algorithm along with two state of the art general purpose sampling algorithms, likelihood weighting (Fung & Chang, 1989; Shachter & Peot, 1989) and self-importance sampling (Shachter & Peot, 1989). We used in our tests three large real Bayesian network models available to the scientific community: the CPCS network (Pradhan et al., 1994), the PATHFINDER network (Heckerman, Horvitz, & Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, & Druzdzel, 1997), with evidence as unlikely as 10-41. While the AIS-BN algorithm always performed better than the other two algorithms, in the majority of the test cases it achieved orders of magnitude improvement in precision of the results. Improvement in speed given a desired precision is even more dramatic, although we are unable to report numerical results here, as the other algorithms almost never achieved the precision reached even by the first few iterations of the AIS-BN algorithm.

Connected through hyperedge HE-1 (induced by source paper "universal marginaliser for deep amortised inference for probabilistic programs"):
  with members:
    - "learning about an exponential amount of conditional distributions"
    - "universal marginalizer for amortised inference and embedding of generative models"
    - "pyro deep universal probabilistic programming"
    - "attention is all you need"
    - "using synthetic data to train neural networks is model based reasoning"
    - "inference compilation and universal probabilistic programming"
    - "deep amortized inference for probabilistic programs"
    - "made masked autoencoder for distribution estimation"

Connected through hyperedge HE-2 (induced by source paper "using deep neural network approximate bayesian network"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "loopy belief propagation for approximate inference an empirical study"
    - "backward simulation in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "a universal marginalizer for amortized inference in generative models"):
  with members:
    - "using synthetic data to train neural networks is model based reasoning"
    - "made masked autoencoder for distribution estimation"
    - "adam a method for stochastic optimization"
    - "recognition networks for approximate inference in bn20 networks"

Connected through hyperedge HE-4 (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"
    - "adaptive importance sampling for estimation in structured domains"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "real time top k predictive query processing over event streams"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "evidence absorption and propagation through evidence reversals"
    - "a bayesian approach to learning causal networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-6 (induced by source paper "confidence inference in bayesian networks"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-7 (induced by source paper "computational investigation of low discrepancy sequences in simulation algorithms for bayesian networks"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-8 (induced by source paper "recognition networks for approximate inference in bn20 networks"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"
    - "variational probabilistic inference and the qmr dt network"

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)