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: "active learning with multiple views"
  Abstract: Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.

Connected through hyperedge HE-1 (induced by source paper "viewal active learning with viewpoint entropy for semantic segmentation"):
  with members:
    - "learning loss for active learning"
    - "cereals cost effective region based active learning for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "cost effective active learning for melanoma segmentation"
    - "cost effective active learning for deep image classification"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "multi modal active learning from human data a deep reinforcement learning approach"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "multimodal machine translation with reinforcement learning"
    - "active one shot learning"
    - "multimodal machine learning a survey and taxonomy"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"

Connected through hyperedge HE-3 (induced by source paper "unsupervised co learning on mathcal g manifolds across irreducible representations"):
  with members:
    - "cryo electron microscopy image analysis using multi frequency vector diffusion maps"
    - "a survey of multi view representation learning"
    - "multi frequency phase synchronization"
    - "multi frequency vector diffusion maps"
    - "a study of the classification of low dimensional data with supervised manifold learning"
    - "fast steerable principal component analysis"
    - "a survey on multi view learning"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "multi view intact space learning"):
  with members:
    - "a survey on multi view learning"
    - "multiview hessian regularization for image annotation"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"
    - "a kernel method for canonical correlation analysis"

Connected through hyperedge HE-5 (induced by source paper "a novel active learning framework for classification using weighted rank aggregation to achieve multiple query criteria"):
  with members:
    - "exploring representativeness and informativeness for active learning"
    - "stochastic rank aggregation"

Connected through hyperedge HE-6 (induced by source paper "multimodal hierarchical dirichlet process based active perception"):
  with members:
    - "symbol emergence in robotics a survey"
    - "caffe convolutional architecture for fast feature embedding"
    - "an information theoretic analysis of thompson sampling"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "modelling observation correlations for active exploration and robust object detection"
    - "near optimal nonmyopic value of information in graphical models"
    - "active learning with statistical models"
    - "the symbol grounding problem"

Connected through hyperedge HE-7 (induced by source paper "a survey on multi view learning"):
  with members:
    - "multi view learning in the presence of view disagreement"
    - "a kernel method for canonical correlation analysis"

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)