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: "categorizing the content of github readme files"
  Abstract: README files play an essential role in shaping a developer's first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the `What' and `How' of a repository is very common, while many README files lack information regarding the purpose and status of a repository. Our multi-label classifier which can predict eight different categories achieves an F1 score of 0.746. This work enables the owners of software repositories to improve the quality of their documentation and it has the potential to make it easier for the software development community to discover relevant information in GitHub README files.

Connected through hyperedge HE-1 (induced by source paper "conflict and cooperation ai research and development in terms of the economy of conventions"):
  with members:
    - "identifying experts in software libraries and frameworks among github users"
    - "scibert pretrained contextualized embeddings for scientific text"
    - "was ist eine professur fuer kuenstliche intelligenz"
    - "an explorative study of github repositories of ai papers"
    - "won t we fix this issue qualitative characterization and automated identification of wontfix issues on github"
    - "estimating defectiveness of source code a predictive model using github content"
    - "what s in a github star understanding repository starring practices in a social coding platform"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"

Connected through hyperedge HE-2 (induced by source paper "higitclass keyword driven hierarchical classification of github repositories"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "universal language model fine tuning for text classification"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "node2vec scalable feature learning for networks"
    - "pte predictive text embedding through large scale heterogeneous text networks"

Connected through hyperedge HE-3 (induced by source paper "do people prefer natural code"):
  with members:
    - "automatically generating documentation for lambda expressions in java"
    - "replication can improve prior results a github study of pull request acceptance"
    - "the adverse effects of code duplication in machine learning models of code"
    - "studying the difference between natural and programming language corpora"
    - "attention is all you need"
    - "a survey of machine learning for big code and naturalness"
    - "on the naturalness of buggy code"
    - "mining idioms from source code"

Connected through hyperedge HE-4 (induced by source paper "wait for it identifying on hold self admitted technical debt"):
  with members:
    - "bug or not bug report classification using n gram idf"
    - "does technical debt lead to the rejection of pull requests"

Connected through hyperedge HE-5 (induced by source paper "automatically generating documentation for lambda expressions in java"):
  with members:
    - "proactive empirical assessment of new language feature adoption via automated refactoring the case of java 8 default methods"

Connected through hyperedge HE-6 (induced by source paper "predicting good configurations for github and stack overflow topic models"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "automated algorithm selection survey and perspectives"
    - "data driven search based software engineering"
    - "sotorrent reconstructing and analyzing the evolution of stack overflow posts"
    - "a case study of algorithm selection for the traveling thief problem"
    - "aslib a benchmark library for algorithm selection"
    - "algorithm selection for combinatorial search problems a survey"

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)