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: "probabilistic temporal reasoning with endogenous change"
  Abstract: This paper presents a probabilistic model for reasoning about the state of a system as it changes over time, both due to exogenous and endogenous influences. Our target domain is a class of medical prediction problems that are neither so urgent as to preclude careful diagnosis nor progress so slowly as to allow arbitrary testing and treatment options. In these domains there is typically enough time to gather information about the patient's state and consider alternative diagnoses and treatments, but the temporal interaction between the timing of tests, treatments, and the course of the disease must also be considered. Our approach is to elicit a qualitative structural model of the patient from a human expert---the model identifies important attributes, the way in which exogenous changes affect attribute values, and the way in which the patient's condition changes endogenously. We then elicit probabilistic information to capture the expert's uncertainty about the effects of tests and treatments and the nature and timing of endogenous state changes. This paper describes the model in the context of a problem in treating vehicle accident trauma, and suggests a method for solving the model based on the technique of sequential imputation. A complementary goal of this work is to understand and synthesize a disparate collection of research efforts all using the name ?probabilistic temporal reasoning.? This paper analyzes related work and points out essential differences between our proposed model and other approaches in the literature.

Connected through hyperedge HE-1 (induced by source paper "a structurally and temporally extended bayesian belief network model definitions properties and modeling techniques"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "properties of bayesian belief network learning algorithms"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "forecasting sleep apnea with dynamic network models"

Connected through hyperedge HE-2 (induced by source paper "a temporal bayesian network for diagnosis and prediction"):
  with members:
    - "time critical reasoning representations and application"
    - "a structurally and temporally extended bayesian belief network model definitions properties and modeling techniques"

Connected through hyperedge HE-3 (induced by source paper "continuous time bayesian networks"):
  with members:
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-4 (induced by source paper "probabilistic hybrid action models for predicting concurrent percept driven robot behavior"):
  with members:
    - "planning with external events"
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "decision theoretic planning with concurrent temporally extended actions"
    - "conformant planning via symbolic model checking"
    - "cc golog towards more realistic logic based robot controllers"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "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)