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: "two evidential data based models for influence maximization in twitter"
  Abstract: An evidential influence measure for Twitter was proposed.Many influence aspects were considered in the proposed measure.Two evidential influence maximization models were introduced.The performance of the proposed models compared to existing ones was shown. Influence maximization is the problem of selecting a set of influential users in the social network. Those users could adopt the product and trigger a large cascade of adoptions through the "word of mouth" effect. In this paper, we propose two evidential influence maximization models for Twitter social network. The proposed approach uses the theory of belief functions to estimate users influence. Furthermore, the proposed influence estimation measure fuses many influence aspects in Twitter, like the importance of the user in the network structure and the popularity of user's tweets (messages). In our experiments, we compare the proposed solutions to existing ones and we show the performance of our models.

Connected through hyperedge HE-1 (induced by source paper "evidential positive opinion influence measures for viral marketing"):
  with members:
    - "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"
    - "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 "independence of sources in social networks"):
  with members:
    - "combining partially independent belief functions"

Connected through hyperedge HE-3 (induced by source paper "a reliability based approach for influence maximization using the evidence theory"):
  with members:
    - "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"
    - "dynamic time warping distance for message propagation classification in twitter"
    - "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.SI(Social and Information Networks)