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: "rulematrix visualizing and understanding classifiers with rules"
  Abstract: With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.

Connected through hyperedge HE-1 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "speculative execution for guided visual analytics"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "innvestigate neural networks"

Connected through hyperedge HE-2 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "a nutritional label for rankings"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"
    - "proxy non discrimination in data driven systems"

Connected through hyperedge HE-3 (induced by source paper "vice visual counterfactual explanations for machine learning models"):
  with members:
    - "the what if tool interactive probing of machine learning models"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "a survey of methods for explaining black box models"
    - "manipulating and measuring model interpretability"
    - "explanation in artificial intelligence insights from the social sciences"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (induced by source paper "t visne interactive assessment and interpretation of t sne projections"):
  with members:
    - "supporting analysis of dimensionality reduction results with contrastive learning"
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "clustrophile 2 guided visual clustering analysis"
    - "a visual interaction framework for dimensionality reduction based data exploration"
    - "dimreader axis lines that explain non linear projections"
    - "t sne cuda gpu accelerated t sne and its applications to modern data"
    - "clustering with t sne provably"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-5 (induced by source paper "the what if tool interactive probing of machine learning models"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "smoothgrad removing noise by adding noise"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "towards better analysis of deep convolutional neural networks"
    - "equality of opportunity in supervised learning"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "the validity generalizability and feasibility of summative evaluation methods in visual analytics"):
  with members:
    - "skylens visual analysis of skyline on multi dimensional data"
    - "what may visualization processes optimize"

Connected through hyperedge HE-7 (induced by source paper "explaining vulnerabilities to adversarial machine learning through visual analytics"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "analyzing the noise robustness of deep neural networks"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "certified defenses for data poisoning attacks"

Connected through hyperedge HE-8 (induced by source paper "an active approach for model interpretation"):
  with members:
    - "why did you do that explaining black box models with inductive synthesis"
    - "open the black box data driven explanation of black box decision systems"
    - "rule induction for global explanation of trained models"
    - "local rule based explanations of black box decision systems"
    - "interpretable explorable approximations of black box models"
    - "interpreting blackbox models via model extraction"
    - "scalable bayesian rule lists"

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)