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: "modanet a large scale street fashion dataset with polygon annotations"
  Abstract: Understanding clothes from a single image has strong commercial and cultural impacts on modern societies. However, this task remains a challenging computer vision problem due to wide variations in the appearance, style, brand and layering of clothing items. We present a new database called ModaNet, a large-scale collection of images based on Paperdoll dataset. Our dataset provides 55,176 street images, fully annotated with polygons on top of the 1 million weakly annotated street images in Paperdoll. ModaNet aims to provide a technical benchmark to fairly evaluate the progress of applying the latest computer vision techniques that rely on large data for fashion understanding. The rich annotation of the dataset allows to measure the performance of state-of-the-art algorithms for object detection, semantic segmentation and polygon prediction on street fashion images in detail. The polygon-based annotation dataset has been released this https URL, we also host the leaderboard at EvalAI: this https URL.

Connected through hyperedge HE-1 (induced by source paper "neural data server a large scale search engine for transfer learning data"):
  with members:
    - "active domain randomization"
    - "an analysis of pre training on object detection"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "meta sim learning to generate synthetic datasets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "learning to generate synthetic data via compositing"
    - "rethinking imagenet pre training"

Connected through hyperedge HE-2 (induced by source paper "lake ice monitoring with webcams and crowd sourced images"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "parsenet looking wider to see better"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "dressing for diverse body shapes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sizenet weakly supervised learning of visual size and fit in fashion images"
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "3d virtual garment modeling from rgb images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "a deep learning system for predicting size and fit in fashion e commerce"

Connected through hyperedge HE-4 (induced by source paper "imp instance mask projection for high accuracy semantic segmentation of things"):
  with members:
    - "panoptic segmentation"
    - "upsnet a unified panoptic segmentation network"
    - "panoptic feature pyramid networks"
    - "group normalization"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"

Connected through hyperedge HE-5 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid"):
  with members:
    - "netizen style commenting on fashion photos dataset and diversity measures"
    - "end to end deep kronecker product matching for person re identification"
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "person re identification with deep similarity guided graph neural network"
    - "videos as space time region graphs"

Connected through hyperedge HE-6 (induced by source paper "fashion iq a new dataset towards retrieving images by natural language feedback"):
  with members:
    - "neural naturalist generating fine grained image comparisons"
    - "robust change captioning"
    - "drill down interactive retrieval of complex scenes using natural language queries"
    - "expressing visual relationships via language"
    - "learning to describe differences between pairs of similar images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"
    - "person search with natural language description"

Connected through hyperedge HE-7 (induced by source paper "the fashion iq dataset retrieving images by combining side information and relative natural language feedback"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"
    - "person search with natural language description"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "creating capsule wardrobes from fashion images"
    - "towards building large scale multimodal domain aware conversation systems"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"

Connected through hyperedge HE-8 (induced by source paper "c gan complementary fashion item recommendation"):
  with members:
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "style2vec representation learning for fashion items from style sets"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "fashion forward forecasting visual style in fashion"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"

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)