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: "a probabilistic logic programming event calculus"
  Abstract: We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-dened temporal combinations of STA. The constraints on the STA that, if satised, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.

Connected through hyperedge HE-1 (induced by source paper "the complex event recognition group"):
  with members:
    - "probabilistic complex event recognition a survey"
    - "event forecasting with pattern markov chains"
    - "online event recognition from moving vessel trajectories"
    - "online learning of event definitions"
    - "incremental learning of event definitions with inductive logic programming"
    - "probabilistic event calculus for event recognition"
    - "on the implementation of the probabilistic logic programming language problog"

Connected through hyperedge HE-2 (induced by source paper "event correlation and forecasting over multivariate streaming sensor data"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"

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"
    - "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"
    - "probabilistic event calculus for event recognition"

Connected through hyperedge HE-4 (induced by source paper "foundations for a probabilistic event calculus"):
  with members:
    - "clingo asp control preliminary report"
    - "probabilistic event calculus for event recognition"
    - "reasoning about noisy sensors and effectors in the situation calculus"

Connected through hyperedge HE-5 (induced by source paper "an event calculus production rule system for reasoning in dynamic and uncertain domains"):
  with members:
    - "reformulating the situation calculus and the event calculus in the general theory of stable models and in answer set programming"
    - "a logic programming approach to activity recognition"

Connected through hyperedge HE-6 (induced by source paper "probabilistic event calculus for event recognition"):
  with members:
    - "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"
    - "logical hidden markov 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)