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: "places an image database for deep scene understanding"
  Abstract: The rise of multi-million-item dataset initiatives has enabled data-hungry machine learning algorithms to reach near-human semantic classification at tasks such as object and scene recognition. Here we describe the Places Database, a repository of 10 million scene photographs, labeled with scene semantic categories and attributes, comprising a quasi-exhaustive list of the types of environments encountered in the world. Using state of the art Convolutional Neural Networks, we provide impressive baseline performances at scene classification. With its high-coverage and high-diversity of exemplars, the Places Database offers an ecosystem to guide future progress on currently intractable visual recognition problems.

Connected through hyperedge HE-1 (induced by source paper "neural architecture search for deep image prior"):
  with members:
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "progressive neural architecture search"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "large scale evolution of image classifiers"

Connected through hyperedge HE-2 (induced by source paper "to balance or not to balance a simple yet effective approach for learning with long tailed distributions"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "boosting few shot visual learning with self supervision"
    - "dynamic curriculum learning for imbalanced data classification"
    - "self supervised generalisation with meta auxiliary learning"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

Connected through hyperedge HE-3 (induced by source paper "residual cnds for grand challenge scene dataset"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training deeper convolutional networks with deep supervision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "to balance or not to balance an embarrassingly simple approach for learning with long tailed distributions"):
  with members:
    - "when does self supervision improve few shot learning"
    - "boosting few shot visual learning with self supervision"
    - "self supervised generalisation with meta auxiliary learning"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "auxiliary tasks in multi task learning"

Connected through hyperedge HE-5 (induced by source paper "hierarchical approach to classify food scenes in egocentric photo streams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "macnet multi scale atrous convolution networks for food places classification in egocentric photo streams"
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scene recognition with cnns objects scales and dataset bias"
    - "object scene convolutional neural networks for event recognition in images"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-6 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "convolutional two stream network fusion for video action recognition"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "object scene convolutional neural networks for event recognition in images"
    - "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
    - "meta des"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "affordance learning in direct perception for autonomous driving"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "carla an open urban driving simulator"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "learning from maps visual common sense for autonomous driving"
    - "end to end learning for self driving cars"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-8 (induced by source paper "tag based semantic features for scene image classification"):
  with members:
    - "unsupervised deep features for privacy image classification"
    - "indoor image representation by high level semantic features"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "locally supervised deep hybrid model for scene recognition"
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "enriching word vectors with subword information"
    - "deep residual learning for image recognition"
    - "multi scale orderless pooling of deep convolutional activation features"

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)