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: "decision support with belief functions theory for seabed characterization"
  Abstract: The seabed characterization from sonar images is a very hard task because of the produced data and the unknown environment, even for an human expert. In this work we propose an original approach in order to combine binary classifiers arising from different kinds of strategies such as one-versus-one or one-versus-rest, usually used in the SVM-classification. The decision functions coming from these binary classifiers are interpreted in terms of belief functions in order to combine these functions with one of the numerous operators of the belief functions theory. Moreover, this interpretation of the decision function allows us to propose a process of decisions by taking into account the rejected observations too far removed from the learning data, and the imprecise decisions given in unions of classes. This new approach is illustrated and evaluated with a SVM in order to classify the different kinds of sediment on image sonar.

Connected through hyperedge HE-1 (induced by source paper "conflict management in information fusion with belief functions"):
  with members:
    - "evidence combination for a large number of sources"
    - "combining partially independent belief functions"
    - "a distance based decision in the credal level"
    - "contradiction measures and specificity degrees of basic belief assignments"
    - "human expert fusion for image classification"
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"

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 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"

Connected through hyperedge HE-3 (induced by source paper "median evidential c means algorithm and its application to community detection"):
  with members:
    - "evidential communities for complex networks"

Connected through hyperedge HE-4 (induced by source paper "a distance based decision in the credal level"):
  with members:
    - "uncertainty in ontology matching a decision rule based approach"

Connected through hyperedge HE-5 (induced by source paper "uncertainty in ontology matching a decision rule based approach"):
  with members:
    - "contradiction measures and specificity degrees of basic belief assignments"

Connected through hyperedge HE-6 (induced by source paper "implementing general belief function framework with a practical codification for low complexity"):
  with members:
    - "practical uses of belief functions"
    - "generalized proportional conflict redistribution rule applied to sonar imagery and radar targets classification"
    - "human expert fusion for image classification"
    - "a new generalization of the proportional conflict redistribution rule stable in terms of decision"
    - "a new probabilistic transformation of belief mass assignment"
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"
    - "proportional conflict redistribution rules for information fusion"
    - "the generalized pignistic transformation"

Connected through hyperedge HE-7 (induced by source paper "belief decision support and reject for textured images characterization"):
  with members:
    - "toward a combination rule to deal with partial conflict and specificity in belief functions theory"

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)