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: "topic modeling of public repositories at scale using names in source code"
  Abstract: Programming languages themselves have a limited number of reserved keywords and character based tokens that define the language specification. However, programmers have a rich use of natural language within their code through comments, text literals and naming entities. The programmer defined names that can be found in source code are a rich source of information to build a high level understanding of the project. The goal of this paper is to apply topic modeling to names used in over 13.6 million repositories and perceive the inferred topics. One of the problems in such a study is the occurrence of duplicate repositories not officially marked as forks (obscure forks). We show how to address it using the same identifiers which are extracted for topic modeling. #R##N#We open with a discussion on naming in source code, we then elaborate on our approach to remove exact duplicate and fuzzy duplicate repositories using Locality Sensitive Hashing on the bag-of-words model and then discuss our work on topic modeling; and finally present the results from our data analysis together with open-access to the source code, tools and datasets.

Connected through hyperedge HE-1 (induced by source paper "a dataset for github repository deduplication"):
  with members:
    - "a complete set of related git repositories identified via community detection approaches based on shared commits"
    - "the adverse effects of code duplication in machine learning models of code"
    - "usage and attribution of stack overflow code snippets in github projects"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "a convolutional attention network for extreme summarization of source code"
    - "structured generative models of natural source code"

Connected through hyperedge HE-2 (induced by source paper "identifying collaborators in large codebases"):
  with members:
    - "a survey of machine learning for big code and naturalness"

Connected through hyperedge HE-3 (induced by source paper "bagminhash minwise hashing algorithm for weighted sets"):
  with members:
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "improved consistent weighted sampling revisited"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "linearized gmm kernels and normalized random fourier features"
    - "consistent weighted sampling made fast small and easy"
    - "optimal discrete uniform generation from coin flips and applications"

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.PL(Programming Languages)