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: "dempster s rule for evidence ordered in a complete directed acyclic graph"
  Abstract: For the case of evidence ordered in a complete directed acyclic graph this paper presents a new algorithm with lower computational complexity for Dempster's rule than that of step-by-step application of Dempster's rule. In this problem, every original pair of evidences, has a corresponding evidence against the simultaneous belief in both propositions. In this case, it is uncertain whether the propositions of any two evidences are in logical conflict. The original evidences are associated with the vertices and the additional evidences are associated with the edges. The original evidences are ordered, i.e., for every pair of evidences it is determinable which of the two evidences is the earlier one. We are interested in finding the most probable completely specified path through the graph, where transitions are possible only from lower- to higher-ranked vertices. The path is here a representation for a sequence of states, for instance a sequence of snapshots of a physical object's track. A completely specified path means that the path includes no other vertices than those stated in the path representation, as opposed to an incompletely specified path that may also include other vertices than those stated. In a hierarchical network of all subsets of the frame, i.e., of all incompletely specified paths, the original and additional evidences support subsets that are not disjoint, thus it is not possible to prune the network to a tree. Instead of propagating belief, the new algorithm reasons about the logical conditions of a completely specified path through the graph. The new algorithm is O(|THETA| log |THETA|), compared to O(|THETA| ** log |THETA|) of the classic brute force algorithm.

Connected through hyperedge HE-1 (induced by source paper "finding a posterior domain probability distribution by specifying nonspecific evidence"):
  with members:
    - "on nonspecific evidence"
    - "cluster based specification techniques in dempster shafer theory"
    - "specifying nonspecific evidence"

Connected through hyperedge HE-2 (induced by source paper "cluster based specification techniques in dempster shafer theory for an evidential intelligence analysis of multipletarget tracks thesis abstract"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "on nonspecific evidence"
    - "cluster based specification techniques in dempster shafer theory"

Connected through hyperedge HE-3 (induced by source paper "applying data mining and machine learning techniques to submarine intelligence analysis"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "on nonspecific evidence"
    - "specifying nonspecific evidence"

Connected through hyperedge HE-4 (induced by source paper "specifying nonspecific evidence"):
  with members:
    - "finding a posterior domain probability distribution by specifying nonspecific evidence"
    - "on nonspecific evidence"
    - "cluster based specification techniques in dempster shafer theory"

Connected through hyperedge HE-5 (induced by source paper "cluster based specification techniques in dempster shafer theory"):
  with members:
    - "on nonspecific evidence"

Connected through hyperedge HE-6 (induced by source paper "fast dempster shafer clustering 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"
    - "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)