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: "magix model agnostic globally interpretable explanations"
  Abstract: Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.

Connected through hyperedge HE-1 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "is attention interpretable"

Connected through hyperedge HE-2 (induced by source paper "interpretability of blackbox machine learning models through dataview extraction and shadow model creation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a framework for understanding unintended consequences of machine learning"
    - "explaining deep learning models a bayesian non parametric approach"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning important features through propagating activation differences"
    - "interpretability via model extraction"
    - "why should i trust you explaining the predictions of any classifier"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-3 (induced by source paper "lifting interpretability performance trade off via automated feature engineering"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "xgboost a scalable tree boosting system"
    - "openml networked science in machine learning"

Connected through hyperedge HE-4 (induced by source paper "safe ml surrogate assisted feature extraction for model learning"):
  with members:
    - "enslaving the algorithm from a right to an explanation to a right to better decisions"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "evaluation of a tree based pipeline optimization tool for automating data science"
    - "xgboost a scalable tree boosting system"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-5 (induced by source paper "enhancing decision tree based interpretation of deep neural networks through l1 orthogonal regularization"):
  with members:
    - "learning less overlapping representations"
    - "interpretable explorable approximations of black box models"
    - "why should i trust you explaining the predictions of any classifier"
    - "adam a method for stochastic optimization"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-6 (induced by source paper "lormika local rule based model interpretability with k optimal associations"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "a unified approach to interpreting model predictions"
    - "why should i trust you explaining the predictions of any classifier"
    - "opus an efficient admissible algorithm for unordered search"

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.AI(Artificial Intelligence)