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: "learning when training data are costly the effect of class distribution on tree induction"
  Abstract: For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the training examples and/or the computational costs associated with learning from them. In such circumstances, one question of practical importance is: if only n training examples can be selected, in what proportion should the classes be represented? In this article we help to answer this question by analyzing, for a fixed training-set size, the relationship between the class distribution of the training data and the performance of classification trees induced from these data. We study twenty-six data sets and, for each, determine the best class distribution for learning. The naturally occurring class distribution is shown to generally perform well when classifier performance is evaluated using undifferentiated error rate (0/1 loss). However, when the area under the ROC curve is used to evaluate classifier performance, a balanced distribution is shown to perform well. Since neither of these choices for class distribution always generates the best-performing classifier, we introduce a "budget-sensitive" progressive sampling algorithm for selecting training examples based on the class associated with each example. An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) classification performance.

Connected through hyperedge HE-1 (induced by source paper "morse code datasets for machine learning"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "toward geometric deep slam"
    - "using synthetic data to train neural networks is model based reasoning"
    - "interleaver design for deep neural networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "tesseract eliminating experimental bias in malware classification across space and time"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "explaining black box android malware detection"
    - "benchmarking crimes an emerging threat in systems security"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "why should i trust you explaining the predictions of any classifier"
    - "reviewer integration and performance measurement for malware detection"

Connected through hyperedge HE-3 (induced by source paper "superensemble classifier for improving predictions in imbalanced datasets"):
  with members:
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "superensemble classifier for learning from imbalanced business school data set"):
  with members:
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"

Connected through hyperedge HE-5 (induced by source paper "cgmos certainty guided minority oversampling"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "reviving threshold moving a simple plug in bagging ensemble for binary and multiclass imbalanced data"):
  with members:
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "untangling adaboost based cost sensitive classification part ii empirical analysis"):
  with members:
    - "untangling adaboost based cost sensitive classification part i theoretical perspective"
    - "double base asymmetric adaboost"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-8 (induced by source paper "untangling adaboost based cost sensitive classification part i theoretical perspective"):
  with members:
    - "untangling adaboost based cost sensitive classification part ii empirical analysis"
    - "double base asymmetric adaboost"
    - "popular ensemble methods an empirical study"

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.AI(Artificial Intelligence)