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: "reasoning about noisy sensors and effectors in the situation calculus"
  Abstract: Agents interacting with an incompletely known world need to be able to reason about the effects of their actions, and to gain further information about that world they need to use sensors of some sort. Unfortunately, both the effects of actions and the information returned from sensors are subject to error. To cope with such uncertainties, the agent can maintain probabilistic beliefs about the state of the world. With probabilistic beliefs the agent will be able to quantify the likelihood of the various outcomes of its actions and is better able to utilize the information gathered from its error-prone actions and sensors. In this paper, we present a model in which we can reason about an agent's probabilistic degrees of belief and the manner in which these beliefs change as various actions are executed. We build on a general logical theory of action developed by Reiter and others, formalized in the situation calculus. We propose a simple axiomatization that captures an agent's state of belief and the manner in which these beliefs change when actions are executed. Our model displays a number of intuitively reasonable properties.

Connected through hyperedge HE-1 (induced by source paper "a first order logic for reasoning about knowledge and probability"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "a knowledge based analysis of the blockchain protocol"
    - "knowledge and common knowledge in a distributed environment"
    - "reasoning about common knowledge with infinitely many agents"
    - "common knowledge revisited"

Connected through hyperedge HE-2 (induced by source paper "towards a logical account of epistemic causality"):
  with members:
    - "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"
    - "counterfactual causality from first principles"
    - "a modification of the halpern pearl definition of causality"
    - "axiomatizing causal reasoning"
    - "causes and explanations a structural model approach part ii explanations"

Connected through hyperedge HE-3 (induced by source paper "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"):
  with members:
    - "robot location estimation in the situation calculus"
    - "reasoning about probabilities in dynamic systems using goal regression"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "probabilistic reasoning with answer sets"
    - "modelling mixed discrete continuous domains for planning"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "on plans with loops and noise"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"

Connected through hyperedge HE-5 (induced by source paper "foundations for a probabilistic event calculus"):
  with members:
    - "a probabilistic logic programming event calculus"
    - "clingo asp control preliminary report"
    - "probabilistic event calculus for event recognition"

Connected through hyperedge HE-6 (induced by source paper "robot location estimation in the situation calculus"):
  with members:
    - "reasoning about probabilities in dynamic systems using goal regression"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "monitoring a complez physical system using a hybrid dynamic bayes net"
    - "probabilistic reasoning with answer sets"
    - "bayesian logic programs"

Connected through hyperedge HE-7 (induced by source paper "reasoning about probabilities in dynamic systems using goal regression"):
  with members:
    - "robot location estimation in the situation calculus"
    - "tractable inference for complex stochastic processes"
    - "symbolic dynamic programming for discrete and continuous state mdps"
    - "formula based probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "sampling first order logical particles"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "discriminative probabilistic models for relational data"
    - "clp bn constraint logic programming for probabilistic knowledge"
    - "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)