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: "feature maps a comprehensible software representation for design pattern detection"
  Abstract: Design patterns are elegant and well-tested solutions to recurrent software development problems. They are the result of software developers dealing with problems that frequently occur, solving them in the same or a slightly adapted way. A pattern’s semantics provide the intent, motivation, and applicability, describing what it does, why it is needed, and where it is useful. Consequently, design patterns encode a well of information. Developers weave this information into their systems whenever they use design patterns to solve problems. This work presents Feature Maps, a flexible human-and machine-comprehensible software representation based on micro-structures. Our algorithm, the Feature-Role Normalization, presses the high-dimensional, in homogeneous vector space of micro-structures into a feature map. We apply these concepts to the problem of detecting instances of design patterns in source code. We evaluate our methodology on four design patterns, a wide range of balanced and imbalanced labeled training data, and compare classical machine learning (Random Forests) with modern deep learning approaches (Convolutional Neural Networks). Feature maps yield robust classifiers even under challenging settings of strongly imbalanced data distributions without sacrificing human comprehensibility. Results suggest that feature maps are an excellent addition in the software analysis toolbox that can reveal useful information hidden in the source code.

Connected through hyperedge HE-1 (induced by source paper "software engineering meets deep learning a literature review"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "on using machine learning to identify knowledge in api reference documentation"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "when deep learning met code search"
    - "a comprehensive study on deep learning bug characteristics"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "csindexbr exploring the brazilian scientific production in computer science"

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.SE(Software Engineering)