human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "probabilistic event calculus for event recognition"):
  Abstract: Symbolic event recognition systems have been successfully applied to a variety of application domains, extracting useful information in the form of events, allowing experts or other systems to monitor and respond when significant events are recognised. In a typical event recognition application, however, these systems often have to deal with a significant amount of uncertainty. In this paper, we address the issue of uncertainty in logic-based event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of the Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we study how probabilistic modelling changes the behaviour of the formalism, affecting its key property, the inertia of fluents. Furthermore, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset for video surveillance.

Includes members:
  Member "a probabilistic logic programming event calculus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the complex event recognition group")
  Member "location based reasoning about complex multi agent behavior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic complex event recognition a survey")
  Member "probabilistic reasoning about actions in nonmonotonic causal theories":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a probabilistic extension of action language bc")
  Member "improving the accuracy and efficiency of map inference for markov logic":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond markov logic efficient mining of prediction rules in large graphs")
  Member "sampling first order logical particles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "counting belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lifted hybrid variational inference")
  Member "on the implementation of the probabilistic logic programming language problog":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "implementing a library for probabilistic programming using non strict non determinism")
  Member "a logic programming approach to activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic complex event recognition a survey")

Question: Please tell me which class the source paper 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)