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 "synthetic oversampling of multi label data based on local label distribution"):
  Abstract: Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare subconcepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

Includes members:
  Member "making classifier chains resilient to class imbalance":
    also connected through other hyperedges:
      (none)
  Member "multi label class imbalanced action recognition in hockey videos via 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "temporal hockey action recognition via pose and optical flows")
  Member "dealing with difficult minority labels in imbalanced mutilabel data sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a snapshot on nonstandard supervised learning problems taxonomy relationships problem transformations and algorithm adaptations")
  Member "remedial hwr tackling multilabel imbalance through label decoupling and data resampling hybridization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tips guidelines and tools for managing multi label datasets the mldr datasets r package and the cometa data repository")
  Member "towards label imbalance in multi label classification with many labels":
    also connected through other hyperedges:
      (none)
  Member "should we really use post hoc tests based on mean ranks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")

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)