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 "transfer learning with bellwethers to find good configurations"):
  Abstract: As software systems grow in complexity, the space of possible configurations grows exponentially. Within this increasing complexity, developers, maintainers, and users cannot keep track of the interactions between all the various configuration options. Finding the optimally performing configuration of a software system for a given setting is challenging. Recent approaches address this challenge by learning performance models based on a sample set of configurations. However, collecting enough data on enough sample configurations can be very expensive since each such sample requires configuring, compiling and executing the entire system against a complex test suite. The central insight of this paper is that choosing a suitable source (a.k.a. "bellwether") to learn from, plus a simple transfer learning scheme will often outperform much more complex transfer learning methods. Using this insight, this paper proposes BEETLE, a novel bellwether based transfer learning scheme, which can identify a suitable source and use it to find near-optimal configurations of a software system. BEETLE significantly reduces the cost (in terms of the number of measurements of sample configuration) to build performance models. We evaluate our approach with 61 scenarios based on 5 software systems and demonstrate that BEETLE is beneficial in all cases. This approach offers a new highwater mark in configuring software systems. Specifically, BEETLE can find configurations that are as good or better as those found by anything else while requiring only 1/7th of the evaluations needed by the state-of-the-art.

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 "scout an experienced guide to find the best cloud configuration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle")
  Member "low level augmented bayesian optimization for finding the best cloud vm":
    also connected through other hyperedges:
      (none)
  Member "bellwethers a baseline method for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "assessing practitioner beliefs about software defect prediction")
  Member "an uncertainty aware approach to optimal configuration of stream processing systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "black or white how to develop an autotuner for memory based analytics extended version")
  Member "why is differential evolution better than grid search for tuning defect predictors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how to dodge complex software analytics")
  Member "tuning for software analytics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "how to improve ai tools by adding in se knowledge experiments with the timelime defect reduction tool")
  Member "learning transferable features with deep adaptation networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")

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)