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 "explainable active learning xal an empirical study of how local explanations impact annotator experience"):
  Abstract: Active Learning (AL) is a human-in-the-loop Machine Learning paradigm favored for its ability to learn with fewer labeled instances, but the model's states and progress remain opaque to the annotators. Meanwhile, many recognize the benefits of model transparency for people interacting with ML models, as reflected by the surge of explainable AI (XAI) as a research field. However, explaining an evolving model introduces many open questions regarding its impact on the annotation quality and the annotator's experience. In this paper, we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator. We conduct an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation). Our study reveals benefits--supporting trust calibration and enabling additional forms of human feedback, and potential drawbacks--anchoring effect and frustration from transparent model limitations--of providing local explanations in AL. We conclude by suggesting directions for developing explanations that better support annotator experience in AL and interactive ML settings.

Includes members:
  Member "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "survlime a method for explaining machine learning survival models")
  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 "stopping active learning based on predicted change of f measure for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "explaining models an empirical study of how explanations impact fairness judgment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "manipulating and measuring model interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "questioning the ai informing design practices for explainable ai user experiences")
  Member "impact of batch size on stopping active learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "early forecasting of text classification accuracy and f measure with active learning")
  Member "a unified approach to interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "machine teaching a new paradigm for building machine learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning")

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)