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: "learning data augmentation strategies for object detection"
  Abstract: Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.

Connected through hyperedge HE-1 (induced by source paper "gradient boosting neural networks grownet"):
  with members:
    - "depth aware video frame interpolation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "catboost unbiased boosting with categorical features"
    - "xgboost a scalable tree boosting system"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "randaugment practical automated data augmentation with a reduced search space"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"
    - "fast autoaugment"
    - "do imagenet classifiers generalize to imagenet"

Connected through hyperedge HE-3 (induced by source paper "randaugment practical data augmentation with no separate search"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "do imagenet classifiers generalize to imagenet"
    - "a fourier perspective on model robustness in computer vision"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "efficientdet scalable and efficient object detection"):
  with members:
    - "searching for mobilenetv3"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "cornernet detecting objects as paired keypoints"
    - "yolo lite a real time object detection algorithm optimized for non gpu computers"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-5 (induced by source paper "adversarial examples improve image recognition"):
  with members:
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "searching for mobilenetv3"
    - "theoretically principled trade off between robustness and accuracy"
    - "fast autoaugment"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning an efficient network for large scale hierarchical object detection with data imbalance 3rd place solution to open images challenge 2019"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "large scale interactive object segmentation with human annotators"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "rethinking imagenet pre training"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "yolov3 an incremental improvement"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-7 (induced by source paper "water supply prediction based on initialized attention residual network"):
  with members:
    - "link prediction with mutual attention for text attributed networks"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "random erasing data augmentation"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "language modeling with gated convolutional networks"
    - "neural machine translation in linear time"

Connected through hyperedge HE-8 (induced by source paper "team pfdet s methods for open images challenge 2019"):
  with members:
    - "lvis a dataset for large vocabulary instance segmentation"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "squeeze and excitation networks"
    - "megdet a large mini batch object detector"
    - "chainercv a library for deep learning in computer vision"

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)