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: "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
  Abstract: Unsupervised learning and supervised learning are key research topics in deep learning. However, as high-capacity supervised neural networks trained with a large amount of labels have achieved remarkable success in many computer vision tasks, the availability of large-scale labeled images reduced the significance of unsupervised learning. Inspired by the recent trend toward revisiting the importance of unsupervised learning, we investigate joint supervised and unsupervised learning in a large-scale setting by augmenting existing neural networks with decoding pathways for reconstruction. First, we demonstrate that the intermediate activations of pretrained large-scale classification networks preserve almost all the information of input images except a portion of local spatial details. Then, by end-to-end training of the entire augmented architecture with the reconstructive objective, we show improvement of the network performance for supervised tasks. We evaluate several variants of autoencoders, including the recently proposed "what-where" autoencoder that uses the encoder pooling switches, to study the importance of the architecture design. Taking the 16-layer VGGNet trained under the ImageNet ILSVRC 2012 protocol as a strong baseline for image classification, our methods improve the validation-set accuracy by a noticeable margin.

Connected through hyperedge HE-1 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "gland instance segmentation using deep multichannel neural networks"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "scaling object detection by transferring classification weights"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "batch normalization is a cause of adversarial vulnerability"
    - "few shot object detection via feature reweighting"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "exploring the limits of weakly supervised pretraining"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "zero annotation object detection with web knowledge transfer"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-3 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-4 (induced by source paper "smoothfool an efficient framework for computing smooth adversarial perturbations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "divide denoise and defend against adversarial attacks"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-5 (induced by source paper "adversarial learning of privacy preserving and task oriented representations"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "learning privacy preserving encodings through adversarial training"
    - "knockoff nets stealing functionality of black box models"
    - "learning to anonymize faces for privacy preserving action detection"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "privacy preserving human activity recognition from extreme low resolution"
    - "faceless person recognition privacy implications in social media"
    - "deep variational information bottleneck"

Connected through hyperedge HE-6 (induced by source paper "pre training with non expert human demonstration for deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "observe and look further achieving consistent performance on atari"
    - "deep reinforcement learning for doom using unsupervised auxiliary tasks"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "starcraft ii a new challenge for reinforcement learning"
    - "deep q learning from demonstrations"
    - "the atari grand challenge dataset"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "jointly pre training with supervised autoencoder and value losses for deep reinforcement learning"):
  with members:
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "daqn deep auto encoder and q network"
    - "self imitation learning"
    - "observe and look further achieving consistent performance on atari"
    - "kickstarting deep reinforcement learning"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "deep reinforcement learning an overview"

Connected through hyperedge HE-8 (induced by source paper "discovery and separation of features for invariant representation learning"):
  with members:
    - "unified adversarial invariance"
    - "invariant representations through adversarial forgetting"
    - "exact rate distortion in autoencoders via echo noise"
    - "ropad robust presentation attack detection through unsupervised adversarial invariance"
    - "niesr nuisance invariant end to end speech recognition"
    - "invariant representations without adversarial training"
    - "unsupervised adversarial invariance"
    - "information constraints on auto encoding variational bayes"

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)