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: "formula based probabilistic inference"
  Abstract: Computing the probability of a formula given the probabilities or weights associated with other formulas is a natural extension of logical inference to the probabilistic setting. Surprisingly, this problem has received little attention in the literature to date, particularly considering that it includes many standard inference problems as special cases. In this paper, we propose two algorithms for this problem: formula decomposition and conditioning, which is an exact method, and formula importance sampling, which is an approximate method. The latter is, to our knowledge, the first application of model counting to approximate probabilistic inference. Unlike conventional variable-based algorithms, our algorithms work in the dual realm of logical formulas. Theoretically, we show that our algorithms can greatly improve efficiency by exploiting the structural information in the formulas. Empirically, we show that they are indeed quite powerful, often achieving substantial performance gains over state-of-the-art schemes.

Connected through hyperedge HE-1 (induced by source paper "variational hybridization and transformation for large inaccurate noisy or networks"):
  with members:
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "addressing the rare word problem in neural machine translation"
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "loopy belief propagation for approximate inference an empirical study"
    - "unsupervised learning of noisy or bayesian networks"
    - "structured priors for structure learning"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-2 (induced by source paper "probabilistic inference modulo theories"):
  with members:
    - "church a language for generative models"
    - "probabilistic theorem proving"

Connected through hyperedge HE-3 (induced by source paper "approximate lifted inference with probabilistic databases"):
  with members:
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "skolemization for weighted first order model counting"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "lifted relax compensate and then recover from approximate to exact lifted probabilistic inference"
    - "probabilistic theorem proving"

Connected through hyperedge HE-4 (induced by source paper "oblivious bounds on the probability of boolean functions"):
  with members:
    - "dissociation and propagation for approximate lifted inference with standard relational database management systems"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "structured message passing"
    - "approximation by quantization"
    - "node splitting a scheme for generating upper bounds in bayesian networks"
    - "optimal upper and lower bounds for boolean expressions by dissociation"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "dissociation and propagation for approximate lifted inference with standard relational database management systems"):
  with members:
    - "approximate lifted inference with probabilistic databases"
    - "linearized and single pass belief propagation"
    - "oblivious bounds on the probability of boolean functions"
    - "uncertainty in crowd data sourcing under structural constraints"
    - "skolemization for weighted first order model counting"
    - "dissociation and propagation for efficient query evaluation over probabilistic databases"
    - "probabilistic databases with markoviews"
    - "node splitting a scheme for generating upper bounds in bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "reasoning about probabilities in dynamic systems using goal regression"):
  with members:
    - "robot location estimation in the situation calculus"
    - "tractable inference for complex stochastic processes"
    - "symbolic dynamic programming for discrete and continuous state mdps"
    - "reasoning about noisy sensors and effectors in the situation calculus"

Connected through hyperedge HE-7 (induced by source paper "reasoning under the principle of maximum entropy for modal logics k45 kd45 and s5"):
  with members:
    - "counting belief propagation"
    - "from statistical knowledge bases to degrees of belief"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-8 (induced by source paper "approximation by quantization"):
  with members:
    - "mini bucket heuristics for improved search"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "expectation propagation for approximate bayesian inference"
    - "context specific independence in bayesian networks"
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "join graph propagation algorithms"
    - "and or multi valued decision diagrams aomdds for graphical models"

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)