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: "fast dempster shafer clustering using a neural network structure"
  Abstract: In this article we study a problem within Dempster-Shafer theory where 2 n — 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scale problems we need a method with lower computational complexity. The neural structure was found to be effective and much faster than iterative optimization for larger problems. While the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems, the metaconflict per cluster and evidence was moderate. The neural structure was able to find a global minimum over ten runs for problem sizes up to six clusters.

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"
    - "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"
    - "on nonspecific evidence"
    - "managing inconsistent intelligence"
    - "creating prototypes for fast classification in dempster shafer clustering"
    - "specifying nonspecific evidence"
    - "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 "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"
    - "simultaneous dempster shafer clustering and gradual determination of number of clusters 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"
    - "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 "a neural network and iterative optimization hybrid for dempster shafer clustering"):
  with members:
    - "on nonspecific evidence"

Connected through hyperedge HE-7 (induced by source paper "simultaneous dempster shafer clustering and gradual determination of number of clusters using a neural network structure"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "a neural network and iterative optimization hybrid for dempster shafer clustering"
    - "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)