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: "an ontology based adaptive personalized e learning system assisted by software agents on cloud storage"
  Abstract: E-learning and online education have made great strides in the recent past. It has moved from a knowledge transfer model to a highly intellect, swift and interactive proposition capable of advanced decision-making abilities. Two challenges have been observed during the exploration of recent developments in e-learning. Firstly, to incorporate e-learning systems effectively in the evolving semantic web environment and secondly, to realize adaptive personalization according to the learner's changing behavior. An ontology-driven system has proposed to implement the Felder-Silverman learning style model in addition to the learning contents, to validate its integration with the semantic web environment. Software agents are employed to monitor the learner's actual learning style and modify them accordingly. The learner's learning style and their modifications are made within the proposed e-learning system. Cloud storage is used as the primary back-end in order to maintain the ontology, databases and other required server resources. To verify the system, comparisons are made between the information presented and adaptive learning styles of the learner along with actions of agents according to learners' behavior. Finally, various conclusions are drawn by exploring the learner's behavior in an adaptive environment for the proposed e-learning system.

Connected through hyperedge HE-1 (induced by source paper "semi automatic terminology ontology learning based on topic modeling"):
  with members:
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"
    - "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:
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"
    - "machine learning approach for text and document mining"

Connected through hyperedge HE-3 (induced by source paper "smart bike sharing system to make the city even smarter"):
  with members:
    - "an ontological learning management system"

Connected through hyperedge HE-4 (induced by source paper "an ontological learning management system"):
  with members:
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"

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:
    - "computational estimate visualisation and evaluation of agent classified rules learning system"
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"
    - "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:
    - "a hybrid approach using ontology similarity and fuzzy logic for semantic question answering"

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.CY(Computers and Society)