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 "whence to learn transferring knowledge in configurable systems using beetle"):
  Abstract: As software systems grow in complexity and the space of possible configurations increases exponentially, finding the near-optimal configuration of a software system becomes challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough sample configurations can be very expensive since each such sample requires configuring, compiling, and executing the entire system using a complex test suite. When learning on new data is too expensive, it is possible to use \textit{Transfer Learning} to "transfer" old lessons to the new context. Traditional transfer learning has a number of challenges, specifically, (a) learning from excessive data takes excessive time, and (b) the performance of the models built via transfer can deteriorate as a result of learning from a poor source. To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a "bellwether"-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data. This paper evaluates BEETLE with 57 different software configuration problems based on five software systems (a video encoder, an SAT solver, a SQL database, a high-performance C-compiler, and a streaming data analytics tool). In each of these cases, BEETLE found configurations that are as good as or better than those found by other state-of-the-art transfer learners while requiring only a fraction ($\frac{1}{7}$th) of the measurements needed by those other methods. Based on these results, we say that BEETLE is a new high-water mark in optimally configuring software.

Includes members:
  Member "finding faster configurations using flash":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning software configuration spaces a systematic literature review")
  Member "micky a cheaper alternative for selecting cloud instances":
    also connected through other hyperedges:
      (none)
  Member "scout an experienced guide to find the best cloud configuration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lynceus tuning and provisioning data analytic jobs on a budget")
  Member "transfer learning for improving model predictions in highly configurable software":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning software configuration spaces a systematic literature review")
  Member "bellwethers a baseline method for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "assessing practitioner beliefs about software defect prediction")
  Member "using bad learners to find good configurations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards quality assurance of software product lines with adversarial configurations")
  Member "bestconfig tapping the performance potential of systems via automatic configuration tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "black or white how to develop an autotuner for memory based analytics extended version")
  Member "faster discovery of faster system configurations with spectral learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning software configuration spaces a systematic literature review")

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)