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: "cross domain image retrieval with attention modeling"
  Abstract: With the proliferation of e-commerce websites and the ubiquitousness of smart phones, cross-domain image retrieval using images taken by smart phones as queries to search products on e-commerce websites is emerging as a popular application. One challenge of this task is to locate the attention of both the query and database images. In particular, database images, e.g. of fashion products, on e-commerce websites are typically displayed with other accessories, and the images taken by users contain noisy background and large variations in orientation and lighting. Consequently, their attention is difficult to locate. In this paper, we exploit the rich tag information available on the e-commerce websites to locate the attention of database images. For query images, we use each candidate image in the database as the context to locate the query attention. Novel deep convolutional neural network architectures, namely TagYNet and CtxYNet, are proposed to learn the attention weights and then extract effective representations of the images. Experimental results on public datasets confirm that our approaches have significant improvement over the existing methods in terms of the retrieval accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "fine grained fashion similarity learning by attribute specific embedding network"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "cbam convolutional block attention module"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "clothing retrieval with visual attention model"
    - "attention is all you need"
    - "automatic spatially aware fashion concept discovery"
    - "learning fashion compatibility with bidirectional lstms"

Connected through hyperedge HE-2 (induced by source paper "one shot image to image translation via part global learning with a multi adversarial framework"):
  with members:
    - "generative adversarial network with multi branch discriminator for cross species image to image translation"
    - "semantic image retrieval by uniting deep neural networks and cognitive architectures"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "one shot unsupervised cross domain translation"
    - "improving shape deformation in unsupervised image to image translation"

Connected through hyperedge HE-3 (induced by source paper "ladder loss for coherent visual semantic embedding"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "stacked cross attention for image text matching"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "vse improving visual semantic embeddings with hard negatives"
    - "local similarity aware deep feature embedding"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-4 (induced by source paper "cross domain image matching in presence of outliers"):
  with members:
    - "cross domain image matching with deep feature maps"
    - "adversarially learned one class classifier for novelty detection"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "anomaly detection using one class neural networks"
    - "cross view image matching for geo localization in urban environments"
    - "deep hashing network for unsupervised domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "asymmetric tri training for unsupervised domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "snap and find deep discrete cross domain garment image retrieval"):
  with members:
    - "automatic spatially aware fashion concept discovery"
    - "hashnet deep learning to hash by continuation"
    - "video captioning with transferred semantic attributes"
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "a survey on learning to hash"
    - "boosting image captioning with attributes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-6 (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"
    - "unconstrained fashion landmark detection via hierarchical recurrent transformer networks"
    - "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-7 (induced by source paper "image to image translation for cross domain disentanglement"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "multimodal unsupervised image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "toward multimodal image to image translation"
    - "disentangled person image generation"

Connected through hyperedge HE-8 (induced by source paper "unsupervised shape transformer for image translation and cross domain retrieval"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "synthesizing images of humans in unseen poses"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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.MM(Multimedia)