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 "phantom curating github for engineered software projects using time series clustering"):
  Abstract: Context: Within the field of Mining Software Repositories, there are numerous methods employed to filter datasets in order to avoid analysing low-quality projects. Unfortunately, the existing filtering methods have not kept up with the growth of existing data sources, such as GitHub, and researchers often rely on quick and dirty techniques to curate datasets. #R##N#Objective: The objective of this study is to develop a method capable of filtering large quantities of software projects in a time-efficient way. #R##N#Method: This study follows the Design Science Research (DSR) methodology. The proposed method, PHANTOM, extracts five measures from Git logs. Each measure is transformed into a time-series, which is represented as a feature vector for clustering using the k-means algorithm. #R##N#Results: Using the ground truth from a previous study, PHANTOM was shown to be able to rediscover the ground truth with up to 0.87 Precision or 0.94 Recall, and be able to identify "well-engineered" projects with up to 0.87 Precision and 0.94 Recall on the validation dataset. PHANTOM downloaded and processed the metadata of 1,786,601 GitHub repositories in 21.5 days, which is over 33\% faster than a similar study, which used a computer cluster of 200 nodes. #R##N#Conclusions: It is possible to use an unsupervised approach to identify well-engineering projects. PHANTOM was shown to be competitive compared to the existing supervised approaches while reducing the hardware requirements by two orders of magnitude.

Includes members:
  Member "a large scale study on the usage of testing patterns that address maintainability attributes patterns for ease of modification diagnoses and comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tests as maintainable assets via auto generated spies a case study involving the scala collections library s iterator trait")
  Member "highly comparative feature based time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complexity measures and features for times series classification")

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)