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: "technical note bias and the quantification of stability"
  Abstract: Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.

Connected through hyperedge HE-1 (induced by source paper "metafeatures based rule extraction for classifiers on behavioral and textual data"):
  with members:
    - "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"
    - "rule induction for global explanation of trained models"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "recommender system for online dating service"

Connected through hyperedge HE-2 (induced by source paper "proving data poisoning robustness in decision trees"):
  with members:
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "certified defenses for data poisoning attacks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "curie a method for protecting svm classifier from poisoning attack"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "types of cost in inductive concept learning"):
  with members:
    - "low size complexity inductive logic programming the east west challenge considered as a problem in cost sensitive classification"
    - "connectionist theory refinement genetically searching the space of network topologies"
    - "active learning with statistical models"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

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)