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 "why is chicago deceptive towards building model driven tutorials for humans"):
  Abstract: To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real-time assistance, we explore model-driven tutorials to help humans understand these patterns in a training phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large-scale, randomized human-subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real-time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human-centered tutorials and explanations towards a synergy between humans and AI.

Includes members:
  Member "explaining machine learning classifiers through diverse counterfactual explanations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "on human predictions with explanations and predictions of machine learning models a case study on deception detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "a human grounded evaluation of shap for alert processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "case based reasoning for assisting domain experts in processing fraud alerts of black box machine learning models")
  Member "rating reliability and bias in news articles does ai assistance help everyone":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what gets echoed understanding the pointers in explanations of persuasive arguments")
  Member "human centered tools for coping with imperfect algorithms during medical decision making":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")
  Member "efficient search for diverse coherent explanations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity")
  Member "local rule based explanations of black box decision systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")

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.HC(Human-Computer Interaction)