human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "ensembles of locally independent prediction models"):
  Abstract: Many ensemble methods encourage their constituent models to be diverse, because ensembling provides no benefits when models are identical. Most methods define diversity in terms of differences in training set predictions. In this paper, however, we demonstrate that diversity in training set predictions does not necessarily imply diversity when extrapolating even slightly outside it (which can affect generalization). To address this issue, we introduce a new diversity metric and associated method of training ensembles of models that extrapolate differently on local patches of the data manifold. Across a variety of synthetic and real-world tasks, we find that our method improves generalization and diversity in qualitatively novel ways, especially under data limits and covariate shift.

Includes members:
  Member "improving adversarial robustness via promoting ensemble diversity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predictive multiplicity in classification")
  Member "how complex is your classification problem a survey on measuring classification complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characterizing the decision boundary of deep neural networks")
  Member "learning qualitatively diverse and interpretable rules for classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enumeration of distinct support vectors for interactive decision making")
  Member "snapshot ensembles train 1 get m for free":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distilled hierarchical neural ensembles with adaptive inference cost")
  Member "learning independent causal mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recurrent independent mechanisms")
  Member "right for the right reasons training differentiable models by constraining their explanations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

Question: Please tell me which class the source paper 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)