human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  Abstract: Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and noisy observations. This can be a hard problem in complex processes with large state spaces. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the process. We consider a specific type of causal relation, called passivity, which pertains to how state variables cause changes in other variables. We present the Passivity-based Selective Belief Filtering (PSBF) method, which maintains a factored belief representation and exploits passivity to perform selective updates over the belief factors. PSBF produces exact belief states under certain assumptions and approximate belief states otherwise, where the approximation error is bounded by the degree of uncertainty in the process. We show empirically, in synthetic processes with varying sizes and degrees of passivity, that PSBF is faster than several alternative methods while achieving competitive accuracy. Furthermore, we demonstrate how passivity occurs naturally in a complex system such as a multi-robot warehouse, and how PSBF can exploit this to accelerate the filtering task.

Includes members:
  Member "vector space analysis of belief state approximation for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "value directed sampling methods for pomdps")
  Member "causal independence for knowledge acquisition and inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "a new look at causal independence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation")
  Member "a generalization of the noisy or model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the belief noisy or model applied to network reliability analysis")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "d separation from theorems to algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "query the model precomputations for efficient inference with bayesian networks")
  Member "the factored frontier algorithm for approximate inference in dbns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")

Question: Please tell me which class the source paper 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)