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 "learning test traces"):
  Abstract: Modern software projects include automated tests written to check the programs' functionality. The set of functions invoked by a test is called the trace of the test, and the action of obtaining a trace is called tracing. There are many tracing tools since traces are useful for a variety of software engineering tasks such as test generation, fault localization, and test execution planning. A major drawback in using test traces is that obtaining them, i.e., tracing, can be costly in terms of computational resources and runtime. Prior work attempted to address this in various ways, e.g., by selectively tracing only some of the software components or compressing the trace on-the-fly. However, all these approaches still require building the project and executing the test in order to get its (partial, possibly compressed) trace. This is still very costly in many cases. In this work, we propose a method to predict the trace of each test without executing it, based only on static properties of the test and the tested program, as well as past experience on different tests. This prediction is done by applying supervised learning to learn the relation between various static features of test and function and the likelihood that one will include the other in its trace. Then, we show how to use the predicted traces in a recent automated troubleshooting paradigm called Learn Diagnose and plan (LDP), instead of the actual, costly-to-obtain, test traces. In a preliminary evaluation on real-world open-source projects, we observe that our prediction quality is reasonable. In addition, using our trace predictions in LDP yields almost the same results comparing to when using real traces, while requiring less overhead.

Includes members:
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "smote synthetic minority over sampling technique":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")

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)