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: "a human grounded evaluation benchmark for local explanations of machine learning"
  Abstract: In order for people to be able to trust and take advantage of the results of advanced machine learning and artificial intelligence solutions for real decision making, people need to be able to understand the machine rationale for given output. Research in explain artificial intelligence (XAI) addresses the aim, but there is a need for evaluation of human relevance and understandability of explanations. Our work contributes a novel methodology for evaluating the quality or human interpretability of explanations for machine learning models. We present an evaluation benchmark for instance explanations from text and image classifiers. The explanation meta-data in this benchmark is generated from user annotations of image and text samples. We describe the benchmark and demonstrate its utility by a quantitative evaluation on explanations generated from a recent machine learning algorithm. This research demonstrates how human-grounded evaluation could be used as a measure to qualify local machine-learning explanations.

Connected through hyperedge HE-1 (induced by source paper "aggregating explanation methods for stable and robust explainability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fairwashing the risk of rationalization"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "interpretable deep learning under fire"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-2 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-3 (induced by source paper "explainability in human agent systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "methods for interpreting and understanding deep neural networks"
    - "textual explanations for self driving vehicles"
    - "explainable neural computation via stack neural module networks"
    - "providing explanations for recommendations in reciprocal environments"
    - "explainable software analytics"
    - "explanation in artificial intelligence insights from the social sciences"

Connected through hyperedge HE-4 (induced by source paper "human grounded evaluations of explanation methods for text classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "looking deeper into deep learning model attribution based explanations of textcnn"
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "understanding convolutional neural networks for text classification"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "innvestigate neural networks"
    - "towards explainable nlp a generative explanation framework for text classification"

Connected through hyperedge HE-5 (induced by source paper "predicting model failure using saliency maps in autonomous driving systems"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "visualbackprop efficient visualization of cnns"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "the elephant in the room"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "not using the car to see the sidewalk quantifying and controlling the effects of context in classification and segmentation"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-6 (induced by source paper "aggregating explainability methods for neural networks stabilizes explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sanity checks for saliency maps"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "evaluating neural network explanation methods using hybrid documents and morphological prediction"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "automatic rule extraction from long short term memory networks"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "interpreting neural networks to improve politeness comprehension"

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.HC(Human-Computer Interaction)