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: "logical hidden markov models"
  Abstract: Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov models to deal with sequences of structured symbols in the form of logical atoms, rather than flat characters.#R##N##R##N#This note formally introduces LOHMMs and presents solutions to the three central inference problems for LOHMMs: evaluation, most likely hidden state sequence and parameter estimation. The resulting representation and algorithms are experimentally evaluated on problems from the domain of bioinformatics.

Connected through hyperedge HE-1 (induced by source paper "state space abstractions for probabilistic inference a systematic review"):
  with members:
    - "stochastic planning and lifted inference"
    - "domain recursion for lifted inference with existential quantifiers"
    - "contextual symmetries in probabilistic graphical models"
    - "weighted positive binary decision diagrams for exact probabilistic inference"
    - "lifted probabilistic inference for asymmetric graphical models"
    - "lifted tree reweighted variational inference"
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "symmetry aware marginal density estimation"

Connected through hyperedge HE-2 (induced by source paper "constraint based inference in probabilistic logic programs"):
  with members:
    - "inference in probabilistic logic programs using lifted explanations"
    - "new liftable classes for first order probabilistic inference"
    - "lifted variable elimination for probabilistic logic programming"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "stochastic logic programs sampling inference and applications"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"

Connected through hyperedge HE-3 (induced by source paper "probabilistic complex event recognition a survey"):
  with members:
    - "going deeper into action recognition a survey"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "a probabilistic logic programming event calculus"
    - "lifted relational neural networks"
    - "complex events recognition under uncertainty in a sensor network"
    - "relational bayesian networks"
    - "location based reasoning about complex multi agent behavior"
    - "a model for reasoning with uncertain rules in event composition systems"

Connected through hyperedge HE-4 (induced by source paper "learning explanatory rules from noisy data"):
  with members:
    - "learning efficient algorithms with hierarchical attentive memory"
    - "adaptive neural compilation"
    - "programming with a differentiable forth interpreter"
    - "differentiable functional program interpreters"
    - "logic tensor networks deep learning and logical reasoning from data and knowledge"
    - "terpret a probabilistic programming language for program induction"
    - "neural programmer inducing latent programs with gradient descent"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "learning discriminative relational features for sequence labeling"):
  with members:
    - "feature construction for relational sequence learning"
    - "high dimensional non linear variable selection through hierarchical kernel learning"

Connected through hyperedge HE-6 (induced by source paper "probabilistic event calculus for event recognition"):
  with members:
    - "a probabilistic logic programming event calculus"
    - "location based reasoning about complex multi agent behavior"
    - "probabilistic reasoning about actions in nonmonotonic causal theories"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "sampling first order logical particles"
    - "counting belief propagation"
    - "on the implementation of the probabilistic logic programming language problog"
    - "a logic programming approach to activity recognition"

Connected through hyperedge HE-7 (induced by source paper "location based reasoning about complex multi agent behavior"):
  with members:
    - "relational bayesian networks"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"
    - "monitoring teams by overhearing a multi agent plan recognition approach"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "bayesian logic programs"

Connected through hyperedge HE-8 (induced by source paper "sampling first order logical particles"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "reasoning about noisy sensors and effectors in the situation calculus"

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)