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: "median evidential c means algorithm and its application to community detection"
  Abstract: Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semi-centrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods.

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"
    - "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 "calibrated model based evidential clustering using bootstrapping"):
  with members:
    - "decision making with belief functions a review"

Connected through hyperedge HE-3 (induced by source paper "a belief combination rule for a large number of sources"):
  with members:
    - "evidence combination for a large number of sources"
    - "ecmdd evidential c medoids clustering with multiple prototypes"
    - "general combination rules for qualitative and quantitative beliefs"
    - "the application of the matrix calculus to belief functions"

Connected through hyperedge HE-4 (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"
    - "dynamic time warping distance for message propagation classification in twitter"
    - "a data based approach to social influence maximization"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "the belief noisy or model applied to network reliability analysis"):
  with members:
    - "ecmdd evidential c medoids clustering with multiple prototypes"
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "evidential em algorithm applied to progressively censored observations"
    - "evidential communities for complex networks"
    - "a generalization of the noisy or model"
    - "the application of the matrix calculus to belief functions"

Connected through hyperedge HE-7 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes"):
  with members:
    - "adaptive imputation of missing values for incomplete pattern classification"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "evidential label propagation algorithm for graphs"):
  with members:
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "evidential em algorithm applied to progressively censored observations"
    - "evidential communities for complex networks"

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)