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: "pool based sequential active learning for regression"
  Abstract: Active learning is a machine learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential active learning for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. Remarkably, this approach can also be integrated with other existing ALR approaches in the literature to further improve the performance. Extensive experiments on 11 UCI, CMU StatLib, and UFL Media Core datasets from various domains verified the effectiveness of our proposed ALR approaches.

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"
    - "affect estimation in 3d space using multi task active learning for regression"
    - "active learning with statistical models"

Connected through hyperedge HE-2 (induced by source paper "an empirical analysis of deep learning for cardinality estimation"):
  with members:
    - "multi attribute selectivity estimation using deep learning"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "state representation learning for control an overview"
    - "towards a hands free query optimizer through deep learning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "the case for learned index structures"

Connected through hyperedge HE-3 (induced by source paper "active learning for regression using greedy sampling"):
  with members:
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "offline eeg based driver drowsiness estimation using enhanced batch mode active learning ebmal for regression"
    - "driver drowsiness estimation from eeg signals using online weighted adaptation regularization for regression owarr"
    - "switching eeg headsets made easy reducing offline calibration effort using active weighted adaptation regularization"

Connected through hyperedge HE-4 (induced by source paper "affect estimation in 3d space using multi task active learning for regression"):
  with members:
    - "active learning for regression using greedy sampling"
    - "active semi supervised transfer learning astl for offline bci calibration"
    - "a survey on multi task learning"
    - "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)