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: "a neural network and iterative optimization hybrid for dempster shafer clustering"
  Abstract: In this paper we extend an earlier result within Dempster-Shafer theory ["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence are clustered into subsets by a neural network structure. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. While the neural method had a much lower computation time than iterative optimization its average clustering performance was not as good. Here, we develop a hybrid of the two methods. We let the neural structure do the initial clustering in order to achieve a high computational performance. Its solution is fed as the initial state to the iterative optimization in order to improve the clustering performance.

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"
    - "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"
    - "fast dempster shafer clustering using a neural network structure"

Connected through hyperedge HE-2 (induced by source paper "conflict based force aggregation"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "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"
    - "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 "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"
    - "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"
    - "fast dempster shafer clustering using a neural network structure"

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)