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: "dynamic time warping distance for message propagation classification in twitter"
  Abstract: Social messages classification is a research domain that has attracted the attention of many researchers in these last years. Indeed, the social message is different from ordinary text because it has some special characteristics like its shortness. Then the development of new approaches for the processing of the social message is now essential to make its classification more efficient. In this paper, we are mainly interested in the classification of social messages based on their spreading on online social networks (OSN). We proposed a new distance metric based on the Dynamic Time Warping distance and we use it with the probabilistic and the evidential k Nearest Neighbors (k-NN) classifiers to classify propagation networks (PrNets) of messages. The propagation network is a directed acyclic graph (DAG) that is used to record propagation traces of the message, the traversed links and their types. We tested the proposed metric with the chosen k-NN classifiers on real world propagation traces that were collected from Twitter social network and we got good classification accuracies.

Connected through hyperedge HE-1 (induced by source paper "evidential positive opinion influence measures for viral marketing"):
  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"
    - "classification of message spreading in a heterogeneous social network"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-2 (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"
    - "adaptive imputation of missing values for incomplete pattern classification"
    - "maximizing positive opinion influence using an evidential approach"
    - "median evidential c means algorithm and its application to community detection"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-3 (induced by source paper "two evidential data based models for influence maximization in twitter"):
  with members:
    - "adaptive imputation of missing values for incomplete pattern classification"
    - "median evidential c means algorithm and its application to community detection"
    - "classification of message spreading in a heterogeneous social network"
    - "a data based approach to social influence maximization"

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)