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: "adaptive imputation of missing values for incomplete pattern classification"
  Abstract: In classification of incomplete pattern, the missing values can either play a crucial role in the class determination, or have only little influence (or eventually none) on the classification results according to the context. We propose a credal classification method for incomplete pattern with adaptive imputation of missing values based on belief function theory. At first, we try to classify the object (incomplete pattern) based only on the available attribute values. As underlying principle, we assume that the missing information is not crucial for the classification if a specific class for the object can be found using only the available information. In this case, the object is committed to this particular class. However, if the object cannot be classified without ambiguity, it means that the missing values play a main role for achieving an accurate classification. In this case, the missing values will be imputed based on the K-nearest neighbor (K-NN) and Self-Organizing Map (SOM) techniques, and the edited pattern with the imputation is then classified. The (original or edited) pattern is classified according to each training class, and the classification results represented by basic belief assignments are fused with proper combination rules for making the credal classification. The object is allowed to belong with different masses of belief to the specific classes and meta-classes (which are particular disjunctions of several single classes). The credal classification captures well the uncertainty and imprecision of classification, and reduces effectively the rate of misclassifications thanks to the introduction of meta-classes. The effectiveness of the proposed method with respect to other classical methods is demonstrated based on several experiments using artificial and real data sets. HighlightsMissing values are adaptively imputed in classification according to context.SOM and K-NN are used for the imputation with admissible computation burden.Ensemble classifier is introduced for credal classification.The imprecision of classification can be well captured using belief functions.The proposed method has been tested by artificial and real data sets.

Connected through hyperedge HE-1 (induced by source paper "a reliability based approach for influence maximization using the evidence theory"):
  with members:
    - "two evidential data based models for influence maximization in twitter"
    - "maximizing positive opinion influence using an evidential approach"
    - "median evidential c means algorithm and its application to community detection"
    - "dynamic time warping distance for message propagation classification in twitter"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-2 (induced by source paper "two evidential data based models for influence maximization in twitter"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "classification of message spreading in a heterogeneous social network"
    - "dynamic time warping distance for message propagation classification in twitter"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-3 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "evidential em algorithm applied to progressively censored observations"
    - "evidential communities for complex networks"
    - "a comparative study of efficient initialization methods for the k means clustering algorithm"
    - "decision support with belief functions theory for seabed characterization"

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)