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: "evidential em algorithm applied to progressively censored observations"
  Abstract: Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information about data. In this paper we present an extension of the E2M method in a particular case of incomplete data, where the loss of information is due to both mixture models and censored observations. The prior uncertain information is expressed by belief functions, while the pseudo-likelihood function is derived based on imprecise observations and prior knowledge. Then E2M method is evoked to maximize the generalized likelihood function to obtain the optimal estimation of parameters. Numerical examples show that the proposed method could effectively integrate the uncertain prior information with the current imprecise knowledge conveyed by the observed data.

Connected through hyperedge HE-1 (induced by source paper "the belief noisy or model applied to network reliability analysis"):
  with members:
    - "ecmdd evidential c medoids clustering with multiple prototypes"
    - "median evidential c means algorithm and its application to community detection"
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "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-2 (induced by source paper "ecmdd evidential c medoids clustering with multiple prototypes"):
  with members:
    - "adaptive imputation of missing values for incomplete pattern classification"
    - "median evidential c means algorithm and its application to community detection"
    - "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-3 (induced by source paper "evidential label propagation algorithm for graphs"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "a similarity based community detection method with multiple prototype representation"
    - "evidential relational clustering using medoids"
    - "evidential communities for complex networks"

Connected through hyperedge HE-4 (induced by source paper "evidential relational clustering using medoids"):
  with members:
    - "median evidential c means algorithm and its application to community detection"
    - "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)