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 interpretable model with globally consistent explanations for credit risk"
  Abstract: We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its explanations.

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:
    - "an evaluation of the human interpretability of explanation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "causality based explanation of classification outcomes"):
  with members:
    - "the shapley value of tuples in query answering"
    - "explaining classifiers with causal concept effect cace"
    - "explainable ai for trees from local explanations to global understanding"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "the complexity of causality and responsibility for query answers and non answers"
    - "responsibility and blame a structural model approach"

Connected through hyperedge HE-3 (induced by source paper "interpreting neural networks using flip points"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "adversarially robust generalization requires more data"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "on calibration of modern neural networks"
    - "interpretable explorable approximations of black box models"
    - "towards proving the adversarial robustness of deep neural networks"
    - "prototypical networks for few shot learning"
    - "swish a self gated activation function"

Connected through hyperedge HE-4 (induced by source paper "maximal adversarial perturbations for obfuscation hiding certain attributes while preserving rest"):
  with members:
    - "distributed generation of privacy preserving data with user customization"
    - "generating user friendly explanations for loan denials using gans"
    - "fair lending needs explainable models for responsible recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "privacy protective gan for face de identification"
    - "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "model agnostic linear competitors when interpretable models compete and collaborate with black box models"):
  with members:
    - "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "on the robustness of interpretability methods"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-6 (induced by source paper "vine visualizing statistical interactions in black box models"):
  with members:
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "consistent individualized feature attribution for tree ensembles"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explanation in artificial intelligence insights from the social sciences"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-7 (induced by source paper "interpretml a unified framework for machine learning interpretability"):
  with members:
    - "interpretability is harder in the multiclass setting axiomatic interpretability for multiclass additive models"
    - "a unified approach to interpreting model predictions"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"):
  with members:
    - "sharp minima can generalize for deep nets"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"

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)