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 "active learning for regression using greedy sampling"):
  Abstract: Regression problems are pervasive in real-world applications. Generally a substantial amount of labeled samples are needed to build a regression model with good generalization ability. However, many times it is relatively easy to collect a large number of unlabeled samples, but time-consuming or expensive to label them. Active learning for regression (ALR) is a methodology to reduce the number of labeled samples, by selecting the most beneficial ones to label, instead of random selection. This paper proposes two new ALR approaches based on greedy sampling (GS). The first approach (GSy) selects new samples to increase the diversity in the output space, and the second (iGS) selects new samples to increase the diversity in both input and output spaces. Extensive experiments on 12 UCI and CMU StatLib datasets from various domains, and on 15 subjects on EEG-based driver drowsiness estimation, verified their effectiveness and robustness.

Includes members:
  Member "active semi supervised transfer learning astl for offline bci calibration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "different set domain adaptation for brain computer interfaces a label alignment approach")
  Member "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised pool based active learning for linear regression")
  Member "pool based sequential active learning for regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised pool based active learning for linear regression")
  Member "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")
  Member "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultra efficient transfer learning with meta update for cross subject eeg classification")

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)