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: "class imbalance problem in data mining review"
  Abstract: In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, data-preprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.

Connected through hyperedge HE-1 (induced by source paper "augmentation scheme for dealing with imbalanced network traffic classification using deep learning"):
  with members:
    - "lstm a search space odyssey"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "a critical review of recurrent neural networks for sequence learning"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "too trivial to test an inverse view on defect prediction to identify methods with low fault risk"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-3 (induced by source paper "adversarial perturbations against real time video classification systems"):
  with members:
    - "drive video analysis for the detection of traffic near miss incidents"
    - "nag network for adversary generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "stacked generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "machine learning for forecasting mid price movement using limit order book data"):
  with members:
    - "data driven neural architecture learning for financial time series forecasting"
    - "benchmark dataset for mid price forecasting of limit order book data with machine learning methods"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "axnet approximate computing using an end to end trainable neural network"):
  with members:
    - "learning internal representations"
    - "nips 2016 tutorial generative adversarial networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-6 (induced by source paper "pca based missing information imputation for real time crash likelihood prediction under imbalanced data"):
  with members:
    - "a tensor based method for missing traffic data completion"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "building emotional machines recognizing image emotions through deep neural networks"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "online and offline domain adaptation for reducing bci calibration effort"):
  with members:
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"
    - "transfer learning in brain computer interfaces"

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)