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: "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"
  Abstract: Extends an earlier result (Johan, 1998) within Dempster-Shafer theory where several pieces of evidence were clustered into a fixed number of clusters using a neural structure. This was done by minimizing a metaconflict function. We now develop a method for simultaneous clustering and determination of number of clusters during iteration in the neural structure. We let the output signals of neurons represent the degree to which pieces of evidence belong to a corresponding cluster. From these we derive a probability distribution regarding the number of clusters, which gradually during the iteration is transformed into a determination of number of clusters. This gradual determination is fed back into the neural structure at each iteration to influence the clustering process.

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"
    - "on nonspecific evidence"
    - "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"

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"
    - "on nonspecific 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"

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"
    - "on nonspecific evidence"
    - "managing inconsistent intelligence"
    - "creating prototypes for fast classification in dempster shafer clustering"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"

Connected through hyperedge HE-4 (induced by source paper "reliable force aggregation using a refined evidence specification from dempster shafer clustering"):
  with members:
    - "practical uses of belief functions"
    - "on nonspecific evidence"
    - "managing inconsistent intelligence"
    - "dempster shafer clustering using potts spin mean field theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"

Connected through hyperedge HE-5 (induced by source paper "clustering belief functions based on attracting and conflicting metalevel evidence"):
  with members:
    - "on nonspecific evidence"
    - "managing inconsistent intelligence"
    - "dempster shafer clustering using potts spin mean field theory"
    - "specifying nonspecific evidence"
    - "fast dempster shafer clustering using a neural network structure"

Connected through hyperedge HE-6 (induced by source paper "fast dempster shafer clustering using a neural network structure"):
  with members:
    - "dempster s rule for evidence ordered in a complete directed acyclic graph"
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "applying data mining and machine learning techniques to submarine intelligence analysis"
    - "on nonspecific evidence"
    - "creating prototypes for fast classification in dempster shafer clustering"
    - "cluster based specification techniques in dempster shafer theory"
    - "specifying nonspecific evidence"

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)