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: "whittlesearch interactive image search with relative attribute feedback"
  Abstract: We propose a novel mode of feedback for image search, where a user describes which properties of exemplar images should be adjusted in order to more closely match his/her mental model of the image sought. For example, perusing image results for a query "black shoes", the user might state, "Show me shoe images like these, but sportier." Offline, our approach first learns a set of ranking functions, each of which predicts the relative strength of a nameable attribute in an image (e.g., sportiness). At query time, the system presents the user with a set of exemplar images, and the user relates them to his/her target image with comparative statements. Using a series of such constraints in the multi-dimensional attribute space, our method iteratively updates its relevance function and re-ranks the database of images. To determine which exemplar images receive feedback from the user, we present two variants of the approach: one where the feedback is user-initiated and another where the feedback is actively system-initiated. In either case, our approach allows a user to efficiently "whittle away" irrelevant portions of the visual feature space, using semantic language to precisely communicate her preferences to the system. We demonstrate our technique for refining image search for people, products, and scenes, and we show that it outperforms traditional binary relevance feedback in terms of search speed and accuracy. In addition, the ordinal nature of relative attributes helps make our active approach efficient--both computationally for the machine when selecting the reference images, and for the user by requiring less user interaction than conventional passive and active methods.

Connected through hyperedge HE-1 (induced by source paper "dressing for diverse body shapes"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "sizenet weakly supervised learning of visual size and fit in fashion images"
    - "fashion retrieval via graph reasoning networks on a similarity pyramid"
    - "3d virtual garment modeling from rgb images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "360 degree textures of people in clothing from a single image"
    - "multi garment net learning to dress 3d people from images"
    - "a deep learning system for predicting size and fit in fashion e commerce"

Connected through hyperedge HE-2 (induced by source paper "thinking outside the pool active training image creation for relative attributes"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "generative adversarial active learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "active decision boundary annotation with deep generative models"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"

Connected through hyperedge HE-3 (induced by source paper "drill down interactive retrieval of complex scenes using natural language queries"):
  with members:
    - "learning type aware embeddings for fashion compatibility"
    - "stacked cross attention for image text matching"
    - "chat crowd a dialog based platform for visual layout composition"
    - "dialog based interactive image retrieval"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning fashion compatibility with bidirectional lstms"
    - "aligned image word representations improve inductive transfer across vision language tasks"

Connected through hyperedge HE-4 (induced by source paper "texture retrieval in the wild through detection based attributes"):
  with members:
    - "texel att representing and classifying element based textures by attributes"
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "visual search at ebay"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "mask r cnn"
    - "deep filter banks for texture recognition description and segmentation"

Connected through hyperedge HE-5 (induced by source paper "exquisitor interactive learning at large"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "cost effective active learning for deep image classification"
    - "approximate furthest neighbor with application to annulus query"
    - "active decision boundary annotation with deep generative models"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "texel att representing and classifying element based textures by attributes"):
  with members:
    - "a survey of recent advances in texture representation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "mask r cnn"
    - "deep filter banks for texture recognition description and segmentation"
    - "describing textures in the wild"

Connected through hyperedge HE-7 (induced by source paper "one shot item search with multimodal data"):
  with members:
    - "deepstyle multimodal search engine for fashion and interior design"
    - "web scale responsive visual search at bing"
    - "enhancing the performance of convolutional neural networks on quality degraded datasets"
    - "visual search at ebay"
    - "visual discovery at pinterest"
    - "quality resilient deep neural networks"
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-8 (induced by source paper "compare and contrast learning prominent visual differences"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "shallow and deep convolutional networks for saliency prediction"
    - "end to end localization and ranking for relative attributes"
    - "generation and comprehension of unambiguous object descriptions"
    - "understanding image virality"
    - "attribute2image conditional image generation from visual attributes"

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)