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: "looking at outfit to parse clothing"
  Abstract: This paper extends fully-convolutional neural networks (FCN) for the clothing parsing problem. Clothing parsing requires higher-level knowledge on clothing semantics and contextual cues to disambiguate fine-grained categories. We extend FCN architecture with a side-branch network which we refer outfit encoder to predict a consistent set of clothing labels to encourage combinatorial preference, and with conditional random field (CRF) to explicitly consider coherent label assignment to the given image. The empirical results using Fashionista and CFPD datasets show that our model achieves state-of-the-art performance in clothing parsing, without additional supervision during training. We also study the qualitative influence of annotation on the current clothing parsing benchmarks, with our Web-based tool for multi-scale pixel-wise annotation and manual refinement effort to the Fashionista dataset. Finally, we show that the image representation of the outfit encoder is useful for dress-up image retrieval application.

Connected through hyperedge HE-1 (induced by source paper "pose guided fashion image synthesis using deep generative model"):
  with members:
    - "a variational u net for conditional appearance and shape generation"
    - "everybody dance now"
    - "viton an image based virtual try on network"
    - "pose guided person image generation"
    - "a generative model of people in clothing"
    - "be your own prada fashion synthesis with structural coherence"
    - "visual search at ebay"
    - "towards accurate multi person pose estimation in the wild"

Connected through hyperedge HE-2 (induced by source paper "freelabel a publicly available annotation tool based on freehand traces"):
  with members:
    - "superpixels an evaluation of the state of the art"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "semantic segmentation refinement by monte carlo region growing of high confidence detections"
    - "iteratively trained interactive segmentation"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "apple flower detection using deep convolutional networks"
    - "mask r cnn"

Connected through hyperedge HE-3 (induced by source paper "searching for apparel products from images in the wild"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "in defense of the triplet loss for person re identification"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-4 (induced by source paper "color inference from semantic labeling for person search in videos"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "macro micro adversarial network for human parsing"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "recommending outfits from personal closet"):
  with members:
    - "pose guided person image generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "mining fashion outfit composition using an end to end deep learning approach on set data"
    - "modeling visual compatibility through hierarchical mid level elements"
    - "learning visual clothing style with heterogeneous dyadic co occurrences"
    - "image based recommendations on styles and substitutes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "a deep learning based fashion attributes detection model"):
  with members:
    - "video2shop exactly matching clothes in videos to online shopping images"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "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"

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)