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: "learning credible models"
  Abstract: In many settings, it is important that a model be capable of providing reasons for its predictions (ie, the model must be interpretable). However, the model's reasoning may not conform with well-established knowledge. In such cases, while interpretable, the model lacks credibility. In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible. In particular, we propose a regularization penalty, expert yielded estimates (EYE), that incorporates expert knowledge about well-known relationships among covariates and the outcome of interest. We give both theoretical and empirical results comparing our proposed method to several other regularization techniques. Across a range of settings, experiments on both synthetic and real data show that models learned using the EYE penalty are significantly more credible than those learned using other penalties. Applied to two large-scale patient risk stratification task, our proposed technique results in a model whose top features overlap significantly with known clinical risk factors, while still achieving good predictive performance.

Connected through hyperedge HE-1 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "compositional fairness constraints for graph embeddings"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-2 (induced by source paper "learning credible deep neural networks with rationale regularization"):
  with members:
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "investigating backtranslation in neural machine translation"
    - "women also snowboard overcoming bias in captioning models"
    - "annotation artifacts in natural language inference data"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "techniques for interpretable machine learning"

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)