human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the feasibility of transfer learning code smells using deep learning"):
  Abstract: Context: A substantial amount of work has been done to detect smells in source code using metrics-based and heuristics-based methods. Machine learning methods have been recently applied to detect source code smells; however, the current practices are considered far from mature. Objective: First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering, just by feeding the source code in tokenized form. Second, investigate the possibility of applying transfer-learning in the context of deep learning models for smell detection. Method: We use existing metric-based state-of-the-art methods for detecting three implementation smells and one design smell in C# code. Using these results as the annotated gold standard, we train smell detection models on three different deep learning architectures. These architectures use Convolution Neural Networks (CNNs) of one or two dimensions, or Recurrent Neural Networks (RNNs) as their principal hidden layers. For the first objective of our study, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples. We perform the experiments with various combinations of hyper-parameters for each model. Results: We find it feasible to detect smells using deep learning methods. Our comparative experiments find that there is no clearly superior method between CNN-1D and CNN-2D. We also observe that performance of the deep learning models is smell-specific. Our transfer-learning experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available.

Includes members:
  Member "a machine learning based ensemble method for anti patterns detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recommendation of move method refactoring using path based representation of code")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "a survey of machine learning for big code and naturalness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "a syntactic neural model for general purpose code generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "easy over hard a case study on deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "classifying and segmenting microscopy images with deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning from web data with self organizing memory module")
  Member "latent predictor networks for code generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi modal attention network learning for semantic source code retrieval")
  Member "sk_p a neural program corrector for moocs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of pyramid structure for code correction")

Question: Please tell me which class the source paper 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.SE(Software Engineering)