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 "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"):
  Abstract: Many machine learning projects for new application areas involve teams of humans who label data for a particular purpose, from hiring crowdworkers to the paper's authors labeling the data themselves. Such a task is quite similar to (or a form of) structured content analysis, which is a longstanding methodology in the social sciences and humanities, with many established best practices. In this paper, we investigate to what extent a sample of machine learning application papers in social computing --- specifically papers from ArXiv and traditional publications performing an ML classification task on Twitter data --- give specific details about whether such best practices were followed. Our team conducted multiple rounds of structured content analysis of each paper, making determinations such as: Does the paper report who the labelers were, what their qualifications were, whether they independently labeled the same items, whether inter-rater reliability metrics were disclosed, what level of training and/or instructions were given to labelers, whether compensation for crowdworkers is disclosed, and if the training data is publicly available. We find a wide divergence in whether such practices were followed and documented. Much of machine learning research and education focuses on what is done once a "gold standard" of training data is available, but we discuss issues around the equally-important aspect of whether such data is reliable in the first place.

Includes members:
  Member "about ml annotation and benchmarking on understanding and transparency of machine learning lifecycles":
    also connected through other hyperedges:
      (none)
  Member "ores lowering barriers with participatory machine learning in wikipedia":
    also connected through other hyperedges:
      (none)
  Member "decision provenance harnessing data flow for accountable systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trustworthy ai in the age of pervasive computing and big data")
  Member "towards traceability in data ecosystems using a bill of materials model":
    also connected through other hyperedges:
      (none)
  Member "measurement and fairness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a hierarchy of limitations in machine learning")
  Member "datasheets for datasets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "the dataset nutrition label a framework to drive higher data quality standards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explainability fact sheets a framework for systematic assessment of explainable approaches")
  Member "multilingual twitter sentiment classification the role of human annotators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a new statistical approach for comparing algorithms for lexicon based sentiment analysis")

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.CY(Computers and Society)