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: "toward a combination rule to deal with partial conflict and specificity in belief functions theory"
  Abstract: We present and discuss a mixed conjunctive and disjunctive rule, a generalization of conflict repartition rules, and a combination of these two rules. In the belief functions theory one of the major problem is the conflict repartition enlightened by the famous Zadeh's example. To date, many combination rules have been proposed in order to solve a solution to this problem. Moreover, it can be important to consider the specificity of the responses of the experts. Since few year some unification rules are proposed. We have shown in our previous works the interest of the proportional conflict redistribution rule. We propose here a mixed combination rule following the proportional conflict redistribution rule modified by a discounting procedure. This rule generalizes many combination rules.

Connected through hyperedge HE-1 (induced by source paper "idcr improved dempster combination rule for multisensor fault diagnosis"):
  with members:
    - "proportional conflict redistribution rules for information fusion"

Connected through hyperedge HE-2 (induced by source paper "conflict management in information fusion with belief functions"):
  with members:
    - "evidence combination for a large number of sources"
    - "combining partially independent belief functions"
    - "a distance based decision in the credal level"
    - "contradiction measures and specificity degrees of basic belief assignments"
    - "human expert fusion for image classification"
    - "decision support with belief functions theory for seabed characterization"

Connected through hyperedge HE-3 (induced by source paper "combining partially independent belief functions"):
  with members:
    - "the nature of the unnormalized beliefs encountered in the transferable belief model"

Connected through hyperedge HE-4 (induced by source paper "general combination rules for qualitative and quantitative beliefs"):
  with members:
    - "a new generalization of the proportional conflict redistribution rule stable in terms of decision"
    - "qualitative belief conditioning rules qbcr"
    - "adaptative combination rule and proportional conflict redistribution rule for information fusion"
    - "proportional conflict redistribution rules for information fusion"

Connected through hyperedge HE-5 (induced by source paper "implementing general belief function framework with a practical codification for low complexity"):
  with members:
    - "practical uses of belief functions"
    - "generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification"
    - "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"
    - "proportional conflict redistribution rules for information fusion"
    - "the generalized pignistic transformation"

Connected through hyperedge HE-6 (induced by source paper "decision support with belief functions theory for seabed characterization"):
  with members:
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-7 (induced by source paper "belief decision support and reject for textured images characterization"):
  with members:
    - "decision support with belief functions theory for seabed characterization"

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)