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 "the belief noisy or model applied to network reliability analysis"):
  Abstract: One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quan-tification step where the Conditional Probability Tables (CPTs) are determined. The number of parameters included in CPTs increases exponentially with the number of parent variables. The most common solution is the application of the so-called canonical gates. The Noisy-OR (NOR) gate, which takes advantage of the independence of causal interactions, provides a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both aleatory and epistemic uncertainty of the network. Compared with NOR, more rich information which is of great value for making decisions can be got when the available knowledge is uncertain. Specially, when there is no epistemic uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR are presented in this paper in order to meet various needs of engineers. The application of BNOR model on the reliability evaluation problem of networked systems demonstrates its effectiveness.

Includes members:
  Member "ecmdd evidential c medoids clustering with multiple prototypes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a belief combination rule for a large number of sources")
  Member "median evidential c means algorithm and its application to community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evidential positive opinion influence measures for viral marketing")
  Member "a similarity based community detection method with multiple prototype representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evidential label propagation algorithm for graphs")
  Member "evidential relational clustering using medoids":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evidential label propagation algorithm for graphs")
  Member "evidential em algorithm applied to progressively censored observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes")
  Member "evidential communities for complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes")
  Member "a generalization of the noisy or model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks")
  Member "the application of the matrix calculus to belief functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effectiveness assessment of cyber physical systems")

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)