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: "sensor validation using dynamic belief networks"
  Abstract: The trajectory of a robot is monitored in a restricted dynamic environment using light beam sensor data. We have a Dynamic Belief Network (DBN), based on a discrete model of the domain, which provides discrete monitoring analogous to conventional quantitative filter techniques. Sensor observations are added to the basic DBN in the form of specific evidence. However, sensor data is often partially or totally incorrect. We show how the basic DBN, which infers only an impossible combination of evidence, may be modified to handle specific types of incorrect data which may occur in the domain. We then present an extension to the DBN, the addition of an invalidating node, which models the status of the sensor as working or defective. This node provides a qualitative explanation of inconsistent data: it is caused by a defective sensor. The connection of successive instances of the invalidating node models the status of a sensor over time, allowing the DBN to handle both persistent and intermittent faults.

Connected through hyperedge HE-1 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "a graph theoretic analysis of information value"
    - "context specific independence in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "bayes networks for sonar sensor fusion"):
  with members:
    - "sample and accumulate algorithms for belief updating in bayes networks"
    - "backward simulation in bayesian networks"
    - "occupancy grids a stochastic spatial representation for active robot perception"

Connected through hyperedge HE-3 (induced by source paper "a computational scheme for reasoning in dynamic probabilistic networks"):
  with members:
    - "optimal decomposition of belief networks"
    - "dynamic network models for forecasting"

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)