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: "streetstyle exploring world wide clothing styles from millions of photos"
  Abstract: Each day billions of photographs are uploaded to photo-sharing services and social media platforms. These images are packed with information about how people live around the world. In this paper we exploit this rich trove of data to understand fashion and style trends worldwide. We present a framework for visual discovery at scale, analyzing clothing and fashion across millions of images of people around the world and spanning several years. We introduce a large-scale dataset of photos of people annotated with clothing attributes, and use this dataset to train attribute classifiers via deep learning. We also present a method for discovering visually consistent style clusters that capture useful visual correlations in this massive dataset. Using these tools, we analyze millions of photos to derive visual insight, producing a first-of-its-kind analysis of global and per-city fashion choices and spatio-temporal trends.

Connected through hyperedge HE-1 (induced by source paper "fashion minimal edits for outfit improvement"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "learning type aware embeddings for fashion compatibility"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "texturegan controlling deep image synthesis with texture patches"
    - "creating capsule wardrobes from fashion images"

Connected through hyperedge HE-2 (induced by source paper "hard aware fashion attribute classification"):
  with members:
    - "msg gan multi scale gradient gan for stable image synthesis"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "imbalanced deep learning by minority class incremental rectification"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "in defense of the triplet loss for person re identification"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"

Connected through hyperedge HE-3 (induced by source paper "toward explainable fashion recommendation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "recommending outfits from personal closet"
    - "explainable fashion recommendation with joint outfit matching and comment generation"
    - "smoothgrad removing noise by adding noise"
    - "on calibration of modern neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning fashion compatibility with bidirectional lstms"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"

Connected through hyperedge HE-4 (induced by source paper "c gan complementary fashion item recommendation"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "geostyle discovering fashion trends and events"):
  with members:
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "recognition in terra incognita"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "mask r cnn"
    - "yfcc100m the new data in multimedia research"
    - "a century of portraits a visual historical record of american high school yearbooks"

Connected through hyperedge HE-6 (induced by source paper "scanner efficient video analysis at scale"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "snorkel rapid training data creation with weak supervision"
    - "end to end learning for self driving cars"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "tensorflow a system for large scale machine learning"
    - "a century of portraits a visual historical record of american high school yearbooks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "a deep learning based fashion attributes detection model"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "looking at outfit to parse clothing"
    - "cross domain image retrieval with attention modeling"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "clothing co parsing by joint image segmentation and labeling"

Connected through hyperedge HE-8 (induced by source paper "interpretable partitioned embedding for customized fashion outfit composition"):
  with members:
    - "changing fashion cultures"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"

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)