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: "generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification"
  Abstract: AbstractIn this chapter, we present two applications in information fusion inorder to evaluate the generalized proportional conﬂict redistribution rulepresented in the chapter [5]. Most of the time the combination rules areevaluated only on simple examples. We study here diﬀerent combinationrules and compare them in terms of decision on real data. Indeed, in realapplications, we need a reliable decision and it is the ﬁnal results thatmatter. Two applications are presented here: a fusion of human expertsopinions on the kind of underwater sediments depict on sonar image anda classiﬁer fusion for radar targets recognition.Keywords: Experts fusion, classiﬁcation, DST, DSmT, generalizedPCR, Sonar, Radar. 1 Introduction We have presented and discussed on some combination rules in the chapter [5].Our study was essentially on the redistribution of conﬂict rules. We have pro-posed a new proportional conﬂict redistribution rule. We have seen that thedecision can be diﬀerent following the rule. Most of the time the combinationrules are evaluated only on simple examples. In this chapter, we study diﬀerentcombination rules and compare them in terms of decision on real data. In-deed, in real applications, we need a reliable decision and it is the ﬁnal resultsthat matter. Hence, for a given application, the best combination rule is therule given the best results. For the decision step, diﬀerent functions such ascredibility, plausibility and pignistic probability [9, 13, 2] are usually used.In this chapter, we present the advantages of the DSmT for the modelizationof real applications and also for the combination step. First, the principles of theDST and DSmT are recalled. We present the formalization of the belief functionmodels, diﬀerent rules of combination and decision. One the combination rule(PCR5) proposed by [12] for two experts is mathematically one of the best forthe proportional redistribution of the conﬂict applicable in the context of theDST and the DSmT. We compare here an extension of this rule for more experts,the PCR6 rule presented in the chapter [5].Two applications are presented here: a fusion of human experts opinions onthe kind of underwater sediments depict on sonar image and a classiﬁer fusionfor radar targets recognition.1

Connected through hyperedge HE-1 (induced by source paper "implementing general belief function framework with a practical codification for low complexity"):
  with members:
    - "practical uses of belief functions"
    - "human expert fusion for image classification"
    - "a new generalization of the proportional conflict redistribution rule stable in terms of decision"
    - "decision support with belief functions theory for seabed characterization"
    - "a new probabilistic transformation of belief mass assignment"
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"
    - "proportional conflict redistribution rules for information fusion"
    - "the generalized pignistic transformation"

Connected through hyperedge HE-2 (induced by source paper "fusion of qualitative beliefs using dsmt"):
  with members:
    - "a symbolic approach to reasoning with linguistic quantifiers"
    - "target type tracking with pcr5 and dempster s rules a comparative analysis"
    - "dsmt a new paradigm shift for information fusion"

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.CV(Computer Vision and Pattern Recognition)