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: "computing upper and lower bounds on likelihoods in intractable networks"
  Abstract: We present deterministic techniques for computing upper and lower bounds on marginal probabilities in sigmoid and noisy-OR networks. These techniques become useful when the size of the network (or clique size) precludes exact computations. We illustrate the tightness of the bounds by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "uncertainty guided continual learning with bayesian neural networks"):
  with members:
    - "large scale incremental learning"
    - "facilitating bayesian continual learning by natural gradients and stein gradients"
    - "overcoming catastrophic forgetting with unlabeled data in the wild"
    - "task free continual learning"
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "efficient lifelong learning with a gem"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-3 (induced by source paper "filtering variational objectives"):
  with members:
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "density estimation using real nvp"
    - "variational inference for monte carlo objectives"
    - "wavenet a generative model for raw audio"
    - "pixel recurrent neural networks"
    - "on the quantitative analysis of decoder based generative models"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-4 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "constructing situation specific belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "using qualitative relationships for bounding probability distributions"):
  with members:
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "incremental tradeoff resolution in qualitative probabilistic networks"
    - "robustness analysis of bayesian networks with local convex sets of distributions"
    - "path planning under time dependent uncertainty"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "propagation of 2 monotone lower probabilities on an undirected graph"
    - "incremental probabilistic inference"

Connected through hyperedge HE-6 (induced by source paper "a general algorithm for approximate inference and its application to hybrid bayes nets"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "hugs combining exact inference and gibbs sampling in junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-7 (induced by source paper "a new class of upper bounds on the log partition function"):
  with members:
    - "on the choice of regions for generalized belief propagation"

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)