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: "hierarchical subquery evaluation for active learning on a graph"
  Abstract: To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. #R##N#Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.

Connected through hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "variational adversarial active learning"
    - "learning loss for active learning"
    - "ranking cgans subjective control over semantic image attributes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-2 (induced by source paper "context aware query selection for active learning in event recognition"):
  with members:
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "temporal convolutional networks for action segmentation and detection"
    - "segmental spatiotemporal cnns for fine grained action segmentation"
    - "action recognition by hierarchical mid level action elements"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "active learning for graph neural networks via node feature propagation"):
  with members:
    - "simplifying graph convolutional networks"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "deep active learning for named entity recognition"
    - "active learning for graph embedding"
    - "neural message passing for quantum chemistry"
    - "a meta learning approach to one step active learning"

Connected through hyperedge HE-4 (induced by source paper "learning loss for active learning"):
  with members:
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "a robust and effective approach towards accurate metastasis detection and pn stage classification in breast cancer"
    - "active self paced learning for cost effective and progressive face identification"
    - "exploring the limits of weakly supervised pretraining"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "representation learning by learning to count"
    - "deep bayesian active learning with image data"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "consistency based semi supervised active learning towards minimizing labeling budget"):
  with members:
    - "active and semi supervised learning in asr benefits on the acoustic and language models"
    - "unsupervised data augmentation for consistency training"
    - "learning loss for active learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-6 (induced by source paper "exploring representativeness and informativeness for active learning"):
  with members:
    - "multi view intact space learning"
    - "efficient active learning of halfspaces an aggressive approach"

Connected through hyperedge HE-7 (induced by source paper "bayesian semi supervised learning with graph gaussian processes"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "give me a hint navigating image databases using human in the loop feedback"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "learning compatibility across categories for heterogeneous item recommendation"
    - "conditional similarity networks"
    - "end to end localization and ranking for relative attributes"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "deep relative attributes"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

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.CV(Computer Vision and Pattern Recognition)