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: "deep active learning over the long tail"
  Abstract: This paper is concerned with pool-based active learning for deep neural networks. Motivated by coreset dataset compression ideas, we present a novel active learning algorithm that queries consecutive points from the pool using farthest-first traversals in the space of neural activation over a representation layer. We show consistent and overwhelming improvement in sample complexity over passive learning (random sampling) for three datasets: MNIST, CIFAR-10, and CIFAR-100. In addition, our algorithm outperforms the traditional uncertainty sampling technique (obtained using softmax activations), and we identify cases where uncertainty sampling is only slightly better than random sampling.

Connected through hyperedge HE-1 (induced by source paper "active learning for entity alignment"):
  with members:
    - "active learning for graph neural networks via node feature propagation"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "lasagne locality and structure aware graph node embedding"
    - "active learning for graph embedding"
    - "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 "information condensing active learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "understanding the limitations of variational mutual information estimators"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "emnist an extension of mnist to handwritten letters"
    - "deep bayesian active learning with image data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "class balanced loss based on effective number of samples"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "feature transfer learning for deep face recognition with under represented data"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the devil is in the tails fine grained classification in the wild"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"

Connected through hyperedge HE-5 (induced by source paper "deep active learning unified and principled method for query and training"):
  with members:
    - "bayesian generative active deep learning"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "variational adversarial active learning"
    - "a principled approach for learning task similarity in multitask learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "adversarial sampling for active learning"
    - "generative adversarial active learning"
    - "learning algorithms for active learning"

Connected through hyperedge HE-6 (induced by source paper "deep batch active learning by diverse uncertain gradient lower bounds"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "adversarial active learning for deep networks a margin based approach"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "libact pool based active learning in python"
    - "determinantal point processes for mini batch diversification"
    - "deep bayesian active learning with image data"
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"

Connected through hyperedge HE-7 (induced by source paper "active learning for deep detection neural networks"):
  with members:
    - "deep visual domain adaptation a survey"
    - "active learning for deep object detection"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep bayesian active learning with image data"
    - "rethinking atrous convolution for semantic image segmentation"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "selectivenet a deep neural network with an integrated reject option"):
  with members:
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "deep active learning with a neural architecture search"
    - "selective classification for deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)