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: "practical uses of belief functions"
  Abstract: We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by ?missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the inter-sensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle ?messy' data problems.

Connected through hyperedge HE-1 (induced by source paper "evidence updating for stream processing in big data robust conditioning in soft and hard fusion environments"):
  with members:
    - "a new approach to updating beliefs"

Connected through hyperedge HE-2 (induced by source paper "implementing general belief function framework with a practical codification for low complexity"):
  with members:
    - "generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification"
    - "human expert fusion for image classification"
    - "a new generalization of the proportional conflict redistribution rule stable in terms of decision"
    - "decision support with belief functions theory for seabed characterization"
    - "a new probabilistic transformation of belief mass assignment"
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"
    - "proportional conflict redistribution rules for information fusion"
    - "the generalized pignistic transformation"

Connected through hyperedge HE-3 (induced by source paper "managing inconsistent intelligence"):
  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"
    - "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-4 (induced by source paper "robust report level cluster to track fusion"):
  with members:
    - "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"
    - "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 "evidential force aggregation"):
  with members:
    - "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"
    - "managing inconsistent intelligence"
    - "robust report level cluster to track fusion"
    - "dempster shafer clustering using potts spin mean field theory"

Connected through hyperedge HE-6 (induced by source paper "reliable force aggregation using a refined evidence specification from dempster shafer clustering"):
  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"
    - "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)