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: "efficient computation of counterfactual explanations of lvq models"
  Abstract: With the increasing use of machine learning in practice and because of legal regulations like EU's GDPR, it becomes indispensable to be able to explain the prediction and behavior of machine learning models. An example of easy to understand explanations of AI models are counterfactual explanations. However, for many models it is still an open research problem how to efficiently compute counterfactual explanations. #R##N#We investigate how to efficiently compute counterfactual explanations of learning vector quantization models. In particular, we propose different types of convex and non-convex programs depending on the used learning vector quantization model.

Connected through hyperedge HE-1 (induced by source paper "convex density constraints for computing plausible counterfactual explanations"):
  with members:
    - "a survey on bias and fairness in machine learning"
    - "face feasible and actionable counterfactual explanations"
    - "interpretable counterfactual explanations guided by prototypes"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "on the computation of counterfactual explanations a survey"
    - "generating contrastive explanations with monotonic attribute functions"
    - "local rule based explanations of black box decision systems"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"

Connected through hyperedge HE-2 (induced by source paper "on the computation of counterfactual explanations a survey"):
  with members:
    - "deep learning for audio signal processing"
    - "face feasible and actionable counterfactual explanations"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "a survey of the usages of deep learning in natural language processing"
    - "explaining explanations an overview of interpretability of machine learning"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "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.LG(Machine Learning)