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 "approximate inference algorithms for hybrid bayesian networks with discrete constraints"):
  Abstract: In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.

Includes members:
  Member "rao blackwellised particle filtering for dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking")
  Member "iterative join graph propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximate knowledge compilation by online collapsed importance sampling")
  Member "on finding minimal w cutset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joins via geometric resolutions worst case and beyond")
  Member "an empirical study of w cutset sampling for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cutset sampling with likelihood weighting")
  Member "a simple insight into iterative belief propagation s success":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "factored probabilistic belief tracking")
  Member "mixtures of deterministic probabilistic networks and their and or search space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "world knowledge as indirect supervision for document clustering")
  Member "expectation propogation for approximate inference in dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")

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)