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: "towards global explanations for credit risk scoring"
  Abstract: In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the original classification accuracy. We use a private residential mortgage default dataset as a use case to illustrate the feasibility of this approach to ensure the decomposability of attributes during pre-processing.

Connected through hyperedge HE-1 (induced by source paper "copying machine learning classifiers"):
  with members:
    - "teacher student compression with generative adversarial networks"
    - "sampling unknown decision functions to build classifier copies"
    - "credit risk prediction in an imbalanced social lending environment"
    - "knowledge distillation in generations more tolerant teachers educate better students"
    - "self normalizing neural networks"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "machine learning models that remember too much"
    - "beyond the technical challenges for deploying machine learning solutions in a software company"

Connected through hyperedge HE-2 (induced by source paper "credit scoring for micro loans"):
  with members:
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a fast and robust tsvm for pattern classification"
    - "xgboost a scalable tree boosting system"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "analysis of classifiers robustness to adversarial perturbations"
    - "fairness through awareness"
    - "learning from labeled and unlabeled data an empirical study across techniques and domains"

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)