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: "the remarkable role of similarity in redundancy based program repair"
  Abstract: Recently, there have been original attempts to use the concept of "code similarity" in program repair, suggesting that similarity analysis has an important role in the repair process. However, there is no dedicated work to characterize and quantify the role of similarity in redundancy-based program repair, where the patch is composed from source code taken from somewhere else. This is where our paper makes a major contribution: we perform a deep and systematic analysis of the role of code similarity during the exploration of the repair search space. We define and set up a large-scale experiment based on four code similarity metrics that capture different similarities: character, token, semantic and structure similarity. Overall, we have computed 56 million similarity score over 15 million source code components. We show that with similarity analysis, at least 90% of search space can be ignored to find the correct patch. Code similarity is capable of ranking the correct repair ingredient first in 4 - 33 % of the considered cases.

Connected through hyperedge HE-1 (induced by source paper "a literature study of embeddings on source code"):
  with members:
    - "import2vec learning embeddings for software libraries"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "semantic embeddings for program behavior patterns"
    - "code2vec learning distributed representations of code"
    - "learning to represent edits"
    - "a cross architecture instruction embedding model for natural language processing inspired binary code analysis"
    - "path based function embedding and its application to specification mining"
    - "automated software vulnerability detection with machine learning"

Connected through hyperedge HE-2 (induced by source paper "arducode predictive framework for automation engineering"):
  with members:
    - "a literature study of embeddings on source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "code2vec learning distributed representations of code"
    - "path based function embedding and its application to specification mining"
    - "automated software vulnerability detection with machine learning"
    - "distributed representations of sentences and documents"

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)