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: "on nonspecific evidence"
  Abstract: When simultaneously reasoning with evidences about several different events it is necessary to separate the evidence according to event. These events should then be handled independently. However, when propositions of evidences are weakly specified in the sense that it may not be certain to which event they are referring, this may not be directly possible. In this paper a criterion for partitioning evidences into subsets representing events is established. This criterion, derived from the conflict within each subset, involves minimising a criterion function for the overall conflict of the partition. An algorithm based on characteristics of the criterion function and an iterative optimisation among partitionings of evidences is proposed.

Connected through hyperedge HE-1 (induced by source paper "managing inconsistent intelligence"):
  with members:
    - "practical uses of belief functions"
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "a neural network and iterative optimization hybrid for dempster shafer clustering"
    - "creating prototypes for fast classification in dempster shafer clustering"
    - "dempster shafer clustering using potts spin mean field theory"
    - "cluster based specification techniques in dempster shafer theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"

Connected through hyperedge HE-2 (induced by source paper "robust report level cluster to track fusion"):
  with members:
    - "practical uses of belief functions"
    - "clustering belief functions based on attracting and conflicting metalevel evidence"
    - "managing inconsistent intelligence"
    - "dempster shafer clustering using potts spin mean field theory"
    - "cluster based specification techniques in dempster shafer theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"
    - "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"

Connected through hyperedge HE-3 (induced by source paper "conflict based force aggregation"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "a neural network and iterative optimization hybrid for dempster shafer clustering"
    - "managing inconsistent intelligence"
    - "creating prototypes for fast classification in dempster shafer clustering"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"
    - "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"

Connected through hyperedge HE-4 (induced by source paper "evidential force aggregation"):
  with members:
    - "practical uses of belief functions"
    - "clustering belief functions based on attracting and conflicting metalevel evidence"
    - "reliable force aggregation using a refined evidence specification from dempster shafer clustering"
    - "conflict based force aggregation"
    - "managing inconsistent intelligence"
    - "robust report level cluster to track fusion"
    - "dempster shafer clustering using potts spin mean field theory"

Connected through hyperedge HE-5 (induced by source paper "reliable force aggregation using a refined evidence specification from dempster shafer clustering"):
  with members:
    - "practical uses of belief functions"
    - "managing inconsistent intelligence"
    - "dempster shafer clustering using potts spin mean field theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"
    - "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"

Connected through hyperedge HE-6 (induced by source paper "clustering belief functions based on attracting and conflicting metalevel evidence"):
  with members:
    - "managing inconsistent intelligence"
    - "dempster shafer clustering using potts spin mean field theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"
    - "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"

Connected through hyperedge HE-7 (induced by source paper "finding a posterior domain probability distribution by specifying nonspecific evidence"):
  with members:
    - "dempster s rule for evidence ordered in a complete directed acyclic graph"
    - "cluster based specification techniques in dempster shafer theory"
    - "specifying nonspecific evidence"

Connected through hyperedge HE-8 (induced by source paper "cluster based specification techniques in dempster shafer theory for an evidential intelligence analysis of multipletarget tracks thesis abstract"):
  with members:
    - "dempster s rule for evidence ordered in a complete directed acyclic graph"
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "cluster based specification techniques in dempster shafer theory"

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)