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: "tractable epistemic reasoning with functional fluents static causal laws and postdiction"
  Abstract: Manfred EppeUniversity of Bremen, Germanymeppe@informatik.uni-bremen.de1. INTRODUCTIONEpistemic reasoning about action and change is a crucial requirement for systems that deal with incom-plete knowledge in the presence of abnormalities, unobservable processes, human-computer interactionand other real-world considerations. A particular type of epistemic inference which is required for di-agnosis tasks in an epistemic context is postiction (Eppe, 2013). Postdiction determines the conditionof an action by observing its effect, and is therefore a fundamental requirement to determine the contextunder which actions occur or to diagnose abnormalities. As an example, consider a smart home 6with arobotic wheelchair that can navigate autonomously within the environment. If the smart home recognizesthat the wheelchair does not arrive at its destination after executing a driving request, it can postdict thatthere must be an abnormality (such as a blocked corridor or a ﬂat tire) that prevents the wheelchair fromdriving. The scenario becomes more complicated if a person is sitting on the wheelchair because hereramiﬁcations (i.e. indirect side-effects of actions) are involved: if the person is sitting on the wheelchair,then it will move as the wheelchair moves.In this work, we are interested in the backward direction of such epistemic ramiﬁcation chains. A smarthome assistance system should be able to postdict that the person is sitting on the wheelchair if the systemobserves that the person’s location changes. This inference is useful to trigger other directly linkedinferences about the conditions of the sit-down action: if the person is not sitting on the wheelchair, thiscould mean that the patient is unconscious and help is required.In addition to incomplete knowledge and ramiﬁcations, the scenario suggests to model knowledge (e.g. aboutthe location of the wheelchair and the person) in a functional manner, which allows for simpler and moreelaboration tolerant modeling of the reasoning domain, as compared to a boolean knowledge model.In order to model and to solve such reasoning problems, we extend the h-approximation (HPX) theory(Eppe, 2013) and present an extended theory called HPX

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)