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: "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"
  Abstract: One of the challenges in information retrieval is providing accurate answers to a user’s question often expressed as uncertainty words. Most answers are based on a Syntactic approach rather than a Semantic analysis of the query. In this paper our objective is to present a hybrid approach for a Semantic question answering retrieval system using Ontology Similarity and Fuzzy logic. We use a Fuzzy Co-clustering algorithm to retrieve collection of documents based on Ontology Similarity. Fuzzy scale uses Fuzzy type-1 for documents and Fuzzy type-2 for words to prioritize answers. The objective of this work is to provide retrieval systems with more accurate answers than non-fuzzy Semantic Ontology approach.

Connected through hyperedge HE-1 (induced by source paper "semi automatic terminology ontology learning based on topic modeling"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"
    - "topic words analysis based on lda model"
    - "algorithms of the lda model report"
    - "tensor decompositions for learning latent variable models"
    - "using variational inference and mapreduce to scale topic modeling"
    - "computing a nonnegative matrix factorization provably"

Connected through hyperedge HE-2 (induced by source paper "blended e learning training belt enhancing railway station controller knowledge"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"
    - "machine learning approach for text and document mining"

Connected through hyperedge HE-3 (induced by source paper "an ontological learning management system"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"

Connected through hyperedge HE-4 (induced by source paper "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"):
  with members:
    - "machine learning approach for text and document mining"

Connected through hyperedge HE-5 (induced by source paper "a system of serial computation for classified rules prediction in non regular ontology trees"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"
    - "computational estimate visualisation and evaluation of agent classified rules learning system"
    - "a differential approach to inference in bayesian networks"
    - "generating conditional probabilities for bayesian networks easing the knowledge acquisition problem"

Connected through hyperedge HE-6 (induced by source paper "ontology based classification and analysis of non emergency smart city events"):
  with members:
    - "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"

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.IR(Information Retrieval)