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 decision boundary annotation with deep generative models"
  Abstract: This paper is on active learning where the goal is to reduce the data annotation burden by interacting with a (human) oracle during training. Standard active learning methods ask the oracle to annotate data samples. Instead, we take a profoundly different approach: we ask for annotations of the decision boundary. We achieve this using a deep generative model to create novel instances along a 1d line. A point on the decision boundary is revealed where the instances change class. Experimentally we show on three data sets that our method can be plugged-in to other active learning schemes, that human oracles can effectively annotate points on the decision boundary, that our method is robust to annotation noise, and that decision boundary annotations improve over annotating data samples.

Connected through hyperedge HE-1 (induced by source paper "thinking outside the pool active training image creation for relative attributes"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generative adversarial active learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"
    - "shape from shading through shape evolution"

Connected through hyperedge HE-2 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "safe exploration in continuous action spaces"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "active inverse reward design"

Connected through hyperedge HE-3 (induced by source paper "active generative adversarial network for image classification"):
  with members:
    - "generative adversarial active learning"
    - "good semi supervised learning that requires a bad gan"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "benchmarking deep reinforcement learning for continuous control"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "exquisitor interactive learning at large"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "cost effective active learning for deep image classification"
    - "approximate furthest neighbor with application to annulus query"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "making learners more monotone"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "minimizers of the empirical risk and risk monotonicity"
    - "model evaluation model selection and algorithm selection in machine learning"

Connected through hyperedge HE-6 (induced by source paper "adversarial sampling for active learning"):
  with members:
    - "learning loss for active learning"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "generative adversarial active learning"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "adversarial active learning for deep networks a margin based approach"):
  with members:
    - "deep bayesian active learning with image data"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "deep active learning for dialogue generation"
    - "towards evaluating the robustness of neural networks"
    - "defensive distillation is not robust to adversarial examples"
    - "theano a python framework for fast computation of mathematical expressions"
    - "qbdc query by dropout committee for training deep supervised architecture"
    - "deepfool a simple and accurate method to fool deep neural networks"

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)