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: "affect estimation in 3d space using multi task active learning for regression"
  Abstract: Acquisition of labeled training samples for affective computing is usually costly and time-consuming, as affects are intrinsically subjective, subtle and uncertain, and hence multiple human assessors are needed to evaluate each affective sample. Particularly, for affect estimation in the 3D space of valence, arousal and dominance, each assessor has to perform the evaluations in three dimensions, which makes the labeling problem even more challenging. Many sophisticated machine learning approaches have been proposed to reduce the data labeling requirement in various other domains, but so far few have considered affective computing. This paper proposes two multi-task active learning for regression approaches, which select the most beneficial samples to label, by considering the three affect primitives simultaneously. Experimental results on the VAM corpus demonstrated that our optimal sample selection approaches can result in better estimation performance than random selection and several traditional single-task active learning approaches. Thus, they can help alleviate the data labeling problem in affective computing, i.e., better estimation performance can be obtained from fewer labeling queries.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pool based active learning for linear regression"):
  with members:
    - "active learning for regression using greedy sampling"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "pool based sequential active learning for regression"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "pool based unsupervised active learning for regression using iterative representativeness diversity maximization irdm"):
  with members:
    - "active learning for regression using greedy sampling"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-3 (induced by source paper "active learning for black box adversarial attacks in eeg based brain computer interfaces"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "active learning for regression using greedy sampling"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "online and offline domain adaptation for reducing bci calibration effort"

Connected through hyperedge HE-4 (induced by source paper "patch learning"):
  with members:
    - "active learning for regression using greedy sampling"
    - "deep residual learning for image recognition"
    - "collaborative deep learning for recommender systems"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "active stacking for heart rate estimation"):
  with members:
    - "active learning for regression using greedy sampling"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-6 (induced by source paper "multi tasking genetic algorithm mtga for fuzzy system optimization"):
  with members:
    - "evolutionary multitasking for single objective continuous optimization benchmark problems performance metric and baseline results"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "online and offline domain adaptation for reducing bci calibration effort"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

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)