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 a dual attribute aware ranking network"
  Abstract: We address the problem of cross-domain image retrieval, considering the following practical application: given a user photo depicting a clothing image, our goal is to retrieve the same or attribute-similar clothing items from online shopping stores. This is a challenging problem due to the large discrepancy between online shopping images, usually taken in ideal lighting/pose/background conditions, and user photos captured in uncontrolled conditions. To address this problem, we propose a Dual Attribute-aware Ranking Network (DARN) for retrieval feature learning. More specifically, DARN consists of two sub-networks, one for each domain, whose retrieval feature representations are driven by semantic attribute learning. We show that this attribute-guided learning is a key factor for retrieval accuracy improvement. In addition, to further align with the nature of the retrieval problem, we impose a triplet visual similarity constraint for learning to rank across the two sub-networks. Another contribution of our work is a large-scale dataset which makes the network learning feasible. We exploit customer review websites to crawl a large set of online shopping images and corresponding offline user photos with fine-grained clothing attributes, i.e., around 450,000 online shopping images and about 90,000 exact offline counterpart images of those online ones. All these images are collected from real-world consumer websites reflecting the diversity of the data modality, which makes this dataset unique and rare in the academic community. We extensively evaluate the retrieval performance of networks in different configurations. The top-20 retrieval accuracy is doubled when using the proposed DARN other than the current popular solution using pre-trained CNN features only (0.570 vs. 0.268).

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 "a strong baseline for fashion retrieval with person re identification models"):
  with members:
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "mildnet a lightweight single scaled deep ranking architecture"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "a strong baseline and batch normalization neck for deep person re identification"
    - "spatial temporal person re identification"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "learning embeddings for product visual search with triplet loss and online sampling"

Connected through hyperedge HE-3 (induced by source paper "efficient multi domain network learning by covariance normalization"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "boosting domain adaptation by discovering latent domains"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "efficient parametrization of multi domain deep neural networks"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "progressive neural networks"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-4 (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-5 (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-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"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "learning to describe differences between pairs of similar images"
    - "composing text and image for image retrieval an empirical odyssey"
    - "dialog based interactive image retrieval"

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"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "the imaterialist fashion attribute dataset"):
  with members:
    - "curriculumnet weakly supervised learning from large scale web images"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "webvision database visual learning and understanding from web data"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "the inaturalist species classification and detection dataset"

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)