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: "unconstrained fashion landmark detection via hierarchical recurrent transformer networks"
  Abstract: Fashion landmarks are functional key points defined on clothes, such as corners of neckline, hemline, and cuff. They have been recently introduced as an effective visual representation for fashion image understanding. However, detecting fashion landmarks are challenging due to background clutters, human poses, and scales. To remove the above variations, previous works usually assumed bounding boxes of clothes are provided in training and test as additional annotations, which are expensive to obtain and inapplicable in practice. This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test. To this end, we present a novel Deep LAndmark Network (DLAN), where bounding boxes and landmarks are jointly estimated and trained iteratively in an end-to-end manner. DLAN contains two dedicated modules, including a Selective Dilated Convolution for handling scale discrepancies, and a Hierarchical Recurrent Spatial Transformer for handling background clutters. To evaluate DLAN, we present a large-scale fashion landmark dataset, namely Unconstrained Landmark Database (ULD), consisting of 30K images. Statistics show that ULD is more challenging than existing datasets in terms of image scales, background clutters, and human poses. Extensive experiments demonstrate the effectiveness of DLAN over the state-of-the-art methods. DLAN also exhibits excellent generalization across different clothing categories and modalities, making it extremely suitable for real-world fashion analysis.

Connected through hyperedge HE-1 (induced by source paper "towards photo realistic virtual try on by adaptively generating leftrightarrow preserving image content"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "fashion minimal edits for outfit improvement"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "foreground aware image inpainting"
    - "semantic image synthesis with spatially adaptive normalization"
    - "fashion editing with multi scale attention normalization"
    - "learning based animation of clothing for virtual try on"
    - "pose guided fashion image synthesis using deep generative model"

Connected through hyperedge HE-2 (induced by source paper "spatial aware non local attention for fashion landmark detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"
    - "non local neural networks"
    - "pose guided person image generation"
    - "attention is all you need"
    - "creating capsule wardrobes from fashion images"
    - "deformable gans for pose based human image generation"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"

Connected through hyperedge HE-3 (induced by source paper "3d virtual garment modeling from rgb images"):
  with members:
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "simulcap single view human performance capture with cloth simulation"
    - "pose guided fashion image synthesis using deep generative model"
    - "instance level human parsing via part grouping network"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "video based reconstruction of 3d people models"
    - "siclope silhouette based clothed people"
    - "dense pose transfer"

Connected through hyperedge HE-4 (induced by source paper "layout graph reasoning for fashion landmark detection"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "automatic spatially aware fashion concept discovery"
    - "learning feature pyramids for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"):
  with members:
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cross domain image retrieval with attention modeling"
    - "mask r cnn"
    - "fashion landmark detection in the wild"
    - "feature pyramid networks for object detection"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "two stream multi task network for fashion recognition"):
  with members:
    - "model free tracking with deep appearance and motion features integration"
    - "leveraging weakly annotated data for fashion image retrieval and label prediction"
    - "learning fashion compatibility with bidirectional lstms"
    - "a survey on multi task learning"
    - "stacked hourglass networks for human pose estimation"
    - "fashion landmark detection in the wild"
    - "convolutional two stream network fusion for video action recognition"
    - "cross domain image retrieval with a dual attribute aware ranking network"

Connected through hyperedge HE-7 (induced by source paper "a global local emebdding module for fashion landmark detection"):
  with members:
    - "viton an image based virtual try on network"
    - "non local neural networks"
    - "be your own prada fashion synthesis with structural coherence"
    - "fashion landmark detection in the wild"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "very deep convolutional networks for large scale image recognition"

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)