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: "deep relative attributes"
  Abstract: Visual attributes are great means of describing images or scenes, in a way both humans and computers understand. In order to establish a correspondence between images and to be able to compare the strength of each property between images, relative attributes were introduced. However, since their introduction, hand-crafted and engineered features were used to learn increasingly complex models for the problem of relative attributes. This limits the applicability of those methods for more realistic cases. We introduce a deep neural network architecture for the task of relative attribute prediction. A convolutional neural network (ConvNet) is adopted to learn the features by including an additional layer (ranking layer) that learns to rank the images based on these features. We adopt an appropriate ranking loss to train the whole network in an end-to-end fashion. Our proposed method outperforms the baseline and state-of-the-art methods in relative attribute prediction on various coarse and fine-grained datasets. Our qualitative results along with the visualization of the saliency maps show that the network is able to learn effective features for each specific attribute. Source code of the proposed method is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "what object should i use task driven object detection"):
  with members:
    - "learning to count objects in natural images for visual question answering"
    - "graph r cnn for scene graph generation"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "videos as space time region graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "soft nms improving object detection with one line of code"
    - "visual question answering as a meta learning task"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-2 (induced by source paper "rank3dgan semantic mesh generation using relative attributes"):
  with members:
    - "multi chart generative surface modeling"
    - "learning category specific mesh reconstruction from image collections"
    - "ranking cgans subjective control over semantic image attributes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "ranking cgans subjective control over semantic image attributes"):
  with members:
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "criteria sliders learning continuous database criteria via interactive ranking"
    - "adversarial ranking for language generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "aligngan learning to align cross domain images with conditional generative adversarial networks"
    - "fader networks manipulating images by sliding attributes"
    - "perceptual losses for real time style transfer and super resolution"
    - "invertible conditional gans for image editing"

Connected through hyperedge HE-7 (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"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "generation and comprehension of unambiguous object descriptions"
    - "understanding image virality"

Connected through hyperedge HE-8 (induced by source paper "real time burst photo selection using a light head adversarial network"):
  with members:
    - "generative adversarial networks an overview"
    - "holstep a machine learning dataset for higher order logic theorem proving"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "conditional generative adversarial nets"
    - "network in network"

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)