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: "scalable bayesian rule lists"
  Abstract: We present an algorithm for building probabilistic rule lists that is two orders of magnitude faster than previous work. Rule list algorithms are competitors for decision tree algorithms. They are associative classifiers, in that they are built from pre-mined association rules. They have a logical structure that is a sequence of IF-THEN rules, identical to a decision list or one-sided decision tree. Instead of using greedy splitting and pruning like decision tree algorithms, we fully optimize over rule lists, striking a practical balance between accuracy, interpretability, and computational speed. The algorithm presented here uses a mixture of theoretical bounds (tight enough to have practical implications as a screening or bounding procedure), computational reuse, and highly tuned language libraries to achieve computational efficiency. Currently, for many practical problems, this method achieves better accuracy and sparsity than decision trees; further, in many cases, the computational time is practical and often less than that of decision trees. The result is a probabilistic classifier (which estimates P(y = 1|x) for each x) that optimizes the posterior of a Bayesian hierarchical model over rule lists.

Connected through hyperedge HE-1 (induced by source paper "interpretation and simplification of deep forest"):
  with members:
    - "searching for mobilenetv3"
    - "explainable machine learning for scientific insights and discoveries"
    - "multi layered gradient boosting decision trees"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "explainable and explicit visual reasoning over scene graphs"

Connected through hyperedge HE-2 (induced by source paper "interpretable multiclass classification by mdl based rule lists"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "structure learning of probabilistic logic programs by searching the clause space"

Connected through hyperedge HE-3 (induced by source paper "learning fair rule lists"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "the bouncer problem challenges to remote explainability"
    - "fairwashing the risk of rationalization"
    - "the dangers of post hoc interpretability unjustified counterfactual explanations"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "faht an adaptive fairness aware decision tree classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "fooling neural network interpretations via adversarial model manipulation"

Connected through hyperedge HE-4 (induced by source paper "mononet towards interpretable models by learning monotonic features"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining explanations an overview of interpretability of machine learning"
    - "on the robustness of interpretability methods"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "explanation in artificial intelligence insights from the social sciences"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-5 (induced by source paper "transparent classification with multilayer logical perceptrons and random binarization"):
  with members:
    - "learning algorithms via neural logic networks"
    - "interpreting cnns via decision trees"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "interpreting deep visual representations via network dissection"
    - "interpretable convolutional neural networks"
    - "distilling a neural network into a soft decision tree"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "the mythos of model interpretability"

Connected through hyperedge HE-6 (induced by source paper "learning interpretable models with causal guarantees"):
  with members:
    - "houdini lifelong learning as program synthesis"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "interpretable explorable approximations of black box models"
    - "learning to infer graphics programs from hand drawn images"
    - "distilling a neural network into a soft decision tree"
    - "interpreting blackbox models via model extraction"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-7 (induced by source paper "hybrid predictive model when an interpretable model collaborates with a black box model"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "interpretable explorable approximations of black box models"
    - "interpretable convolutional neural networks"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "distilling a neural network into a soft decision tree"
    - "harnessing deep neural networks with logic rules"
    - "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 "gaining free or low cost transparency with interpretable partial substitute"):
  with members:
    - "hybrid predictive model when an interpretable model collaborates with a black box model"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "boolean decision rules via column generation"
    - "interpretable explorable approximations of black box models"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "xgboost a scalable tree boosting system"
    - "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.AI(Artificial Intelligence)