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 "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  Abstract: The need for interpretable and accountable intelligent systems grows along with the prevalence of artificial intelligence applications used in everyday life. Explainable intelligent systems are designed to self-explain the reasoning behind system decisions and predictions, and researchers from different disciplines work together to define, design, and evaluate interpretable systems users. However, scholars from different disciplines focus on different objectives and fairly independent topics of interpretable machine learning research, which poses challenges for identifying appropriate design and evaluation methodology and consolidating knowledge from across efforts. To this end, this paper presents a survey and framework intended to share knowledge and experiences of XAI design and evaluation methods across multiple disciplines. Aiming to support diverse design goals and evaluation method in XAI research, after a thorough review of XAI related papers in the fields of machine learning, visualization, and human-computer interaction, we present a categorization of interpretable machine learning design goals and evaluation methods to show a mapping between design goals for different XAI user groups and their evaluation methods. From our findings, we develop a framework with step-by-step design guidelines paired with evaluation methods to close the iterative design and evaluation loop in multidisciplinary XAI teams. Further, we provide summarized ready-to-use tables of evaluation methods and recommendations for different goals in XAI research.

Includes members:
  Member "fairsight visual analytics for fairness in decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "designing tools for semi automated detection of machine learning biases an interview study")
  Member "predicting model failure using saliency maps in autonomous driving systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles")
  Member "human grounded evaluations of explanation methods for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deceptive ai explanations creation and detection")
  Member "quantifying interpretability and trust in machine learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards crisp ml q a machine learning process model with quality assurance methodology")
  Member "summit scaling deep learning interpretability by visualizing activation and attribution summarizations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey")
  Member "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "open issues in combating fake news interpretability as an opportunity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fake news early detection a theory driven model")
  Member "a survey on bias and fairness in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)