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: "an evaluation of the human interpretability of explanation"
  Abstract: Recent years have seen a boom in interest in machine learning systems that can provide a human-understandable rationale for their predictions or decisions. However, exactly what kinds of explanation are truly human-interpretable remains poorly understood. This work advances our understanding of what makes explanations interpretable under three specific tasks that users may perform with machine learning systems: simulation of the response, verification of a suggested response, and determining whether the correctness of a suggested response changes under a change to the inputs. Through carefully controlled human-subject experiments, we identify regularizers that can be used to optimize for the interpretability of machine learning systems. Our results show that the type of complexity matters: cognitive chunks (newly defined concepts) affect performance more than variable repetitions, and these trends are consistent across tasks and domains. This suggests that there may exist some common design principles for explanation systems.

Connected through hyperedge HE-1 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"
    - "efficient search for diverse coherent explanations"

Connected through hyperedge HE-2 (induced by source paper "what would you ask the machine learning model identification of user needs for model explanations based on human model conversations"):
  with members:
    - "do not trust additive explanations"
    - "one explanation does not fit all a toolkit and taxonomy of ai explainability techniques"
    - "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"
    - "machine decisions and human consequences"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "harnessing explanations to bridge ai and humans"):
  with members:
    - "why is chicago deceptive towards building model driven tutorials for humans"
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "learning the difference that makes a difference with counterfactually augmented data"
    - "manipulating and measuring model interpretability"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-4 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"
    - "what clinicians want contextualizing explainable machine learning for clinical end use"

Connected through hyperedge HE-5 (induced by source paper "towards explainable artificial intelligence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "transparency motivations and challenges"
    - "interpretable deep learning in drug discovery"
    - "from clustering to cluster explanations via neural networks"
    - "uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional mri using layer wise relevance propagation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "understanding neural networks via feature visualization a survey"
    - "explaining and interpreting lstms"

Connected through hyperedge HE-6 (induced by source paper "evaluating explanation without ground truth in interpretable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning interpretable models with causal guarantees"
    - "interpretable deep learning in drug discovery"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "neural network attributions a causal perspective"
    - "on the in fidelity and sensitivity for explanations"
    - "interpreting neural networks using flip points"
    - "towards explanation of dnn based prediction with guided feature inversion"

Connected through hyperedge HE-7 (induced by source paper "scalable explanation of inferences on large graphs"):
  with members:
    - "efficient search for diverse coherent explanations"
    - "smoke screener or straight shooter detecting elite sybil attacks in user review social networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "rationalizing neural predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-8 (induced by source paper "how do i fool you manipulating user trust via misleading black box explanations"):
  with members:
    - "learning interpretable models with causal guarantees"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"
    - "interpretability via model extraction"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

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.LG(Machine Learning)