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: "managing inconsistent intelligence"
  Abstract: In this paper we demonstrate that it is possible to manage intelligence in constant time as a pre-process to information fusion through a series of processes dealing with issues such as clustering reports, ranking reports with respect to importance, extraction of prototypes from clusters and immediate classification of newly arriving intelligence reports. These methods are used when intelligence reports arrive which concerns different events which should be handled independently, when it is not known a priori to which event each intelligence report is related. We use clustering that runs as a back-end process to partition the intelligence into subsets representing the events, and in parallel, a fast classification that runs as a front-end process in order to put the newly arriving intelligence into its correct information fusion process.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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 "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"
    - "on nonspecific evidence"
    - "conflict based force aggregation"
    - "robust report level cluster to track fusion"
    - "dempster shafer clustering using potts spin mean field theory"

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"
    - "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-5 (induced by source paper "clustering belief functions based on attracting and conflicting metalevel evidence"):
  with members:
    - "on nonspecific evidence"
    - "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"

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)