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: "tencent ml images a large scale multi label image database for visual representation learning"
  Abstract: In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tags, such as ImageNet. However, a single tag cannot describe all important contents of one image, and some useful visual information may be wasted during training. In this work, we propose to train CNNs from images annotated with multiple tags, to enhance the quality of visual representation of the trained CNN model. To this end, we build a large-scale multi-label image database with 18M images and 11K categories, dubbed Tencent ML-Images. We efficiently train the ResNet-101 model with multi-label outputs on Tencent ML-Images, taking 90 hours for 60 epochs, based on a large-scale distributed deep learning framework,i.e.,TFplus. The good quality of the visual representation of the Tencent ML-Images checkpoint is verified through three transfer learning tasks, including single-label image classification on ImageNet and Caltech-256, object detection on PASCAL VOC 2007, and semantic segmentation on PASCAL VOC 2012. The Tencent ML-Images database, the checkpoints of ResNet-101, and all the training codehave been released at this https URL. It is expected to promote other vision tasks in the research and industry community.

Connected through hyperedge HE-1 (induced by source paper "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"):
  with members:
    - "graph rise graph regularized image semantic embedding"
    - "bottom up object detection by grouping extreme and center points"
    - "neural baby talk"
    - "dropblock a regularization method for convolutional networks"
    - "learning to count objects in natural images for visual question answering"
    - "deep learning for generic object detection a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"

Connected through hyperedge HE-2 (induced by source paper "deep learning on small datasets without pre training using cosine loss"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "tafe net task aware feature embeddings for low shot learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "bag of tricks for image classification with convolutional neural networks"
    - "cosface large margin cosine loss for deep face recognition"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "directional statistics based deep metric learning for image classification and retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "exact adversarial attack to image captioning via structured output learning with latent variables"):
  with members:
    - "target aware deep tracking"
    - "tagging like humans diverse and distinct image annotation"
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-5 (induced by source paper "a benchmark for anomaly segmentation"):
  with members:
    - "learning rich features for image manipulation detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"
    - "deep anomaly detection with outlier exposure"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-6 (induced by source paper "does object recognition work for everyone"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "on fairness and calibration"
    - "recognizing and curating photo albums via event specific image importance"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "algorithmic decision making and the cost of fairness"
    - "word translation without parallel data"

Connected through hyperedge HE-7 (induced by source paper "semantic redundancies in image classification datasets the 10 you don t need"):
  with members:
    - "are all training examples created equal an empirical study"
    - "tensor2tensor for neural machine translation"
    - "not all samples are created equal deep learning with importance sampling"
    - "learning from less data diversified subset selection and active learning in image classification tasks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-8 (induced by source paper "hyper distributed cloud processing for large scale deep learning tasks"):
  with members:
    - "chunkflow distributed hybrid cloud processing of large 3d images by convolutional nets"
    - "yolov3 an incremental improvement"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"
    - "character level convolutional networks for text classification"
    - "you only look once unified real time object detection"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

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)