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: "images don t lie duplicate crowdtesting reports detection with screenshot information"
  Abstract: Abstract   Context: Crowdtesting is effective especially when it comes to the feedback on GUI systems, or subjective opinions about features. Despite of this, we find crowdtesting reports are highly duplicated, i.e., 82% of them are duplicates of others. Most of the existing approaches mainly adopted textual information for duplicate detection, and suffered from low accuracy because of the lexical gap. Our observation on real industrial crowdtesting data found that when dealing with crowdtesting reports of GUI systems, the reports would be accompanied with images, i.e., the screenshots of the tested app. We assume the screenshot to be valuable for duplicate crowdtesting report detection because it reflects the real context of the bug and is not affected by the variety of natural languages.  Objective: We aim at automatically detecting duplicate crowdtesting reports that could help reduce triaging effort.  Method: In this work, we propose SETU which combines information from the ScrEenshots and the TextUal descriptions to detect duplicate crowdtesting reports. We extract four types of features to characterize the screenshots (i.e., image structure feature and image color feature) and the textual descriptions (i.e., TF-IDF feature and word embedding feature), and design a hierarchical algorithm to detect duplicates based on the four similarity scores derived from the four features respectively.  Results: We investigate the effectiveness of SETU on 12 projects with 3,689 reports from one of the Chinese largest crowdtesting platforms. Results show that recall@1 achieved by SETU is 0.44 to 0.79, recall@5 is 0.66 to 0.92, and MAP is 0.21 to 0.58 across all experimental projects. Furthermore, SETU can outperform existing state-of-the-art approaches significantly and substantially.  Conclusion: Through combining the screenshots and textual descriptions, our proposed SETU can improve the duplicate crowdtesting reports detection performance.

Connected through hyperedge HE-1 (induced by source paper "better technical debt detection via surveying"):
  with members:
    - "replication can improve prior results a github study of pull request acceptance"
    - "finding better active learners for faster literature reviews"

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.SE(Software Engineering)