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: "attributes for improved attributes a multi task network for attribute classification"
  Abstract: Attributes, or semantic features, have gained popularity in the past few years in domains ranging from activity recognition in video to face verification. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes. In most works to date, attributes have been considered to be independent. However, we know this not to be the case. Many attributes are very strongly related, such as heavy makeup and wearing lipstick. We propose to take advantage of attribute relationships in three ways: by using a multi-task deep convolutional neural network (MCNN) sharing the lowest layers amongst all attributes, sharing the higher layers for related attributes, and by building an auxiliary network on top of the MCNN which utilizes the scores from all attributes to improve the final classification of each attribute. We demonstrate the effectiveness of our method by producing results on two challenging publicly available datasets.

Connected through hyperedge HE-1 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification"):
  with members:
    - "multi task learning of cascaded cnn for facial attribute classification"
    - "end to end multi task learning with attention"
    - "segment based methods for facial attribute detection from partial faces"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-2 (induced by source paper "a deep multi task learning approach to skin lesion classification"):
  with members:
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "skin disease classification versus skin lesion characterization achieving robust diagnosis using multi label deep neural networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "multi task learning of cascaded cnn for facial attribute classification"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-4 (induced by source paper "curriculum learning of visual attribute clusters for multi task classification"):
  with members:
    - "automated curriculum learning for neural networks"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "teacher student curriculum learning"
    - "curriculum learning for multi task classification of visual attributes"
    - "reverse curriculum generation for reinforcement learning"
    - "class rectification hard mining for imbalanced deep learning"
    - "multiple instance curriculum learning for weakly supervised object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"

Connected through hyperedge HE-5 (induced by source paper "curriculum learning for multi task classification of visual attributes"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "predicting privileged information for height estimation"
    - "contextual action recognition with r cnn"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "hd cnn hierarchical deep convolutional neural network for large scale visual recognition"

Connected through hyperedge HE-6 (induced by source paper "heterogeneous face attribute estimation a deep multi task learning approach"):
  with members:
    - "chalearn looking at people a review of events and resources"
    - "face attribute prediction using off the shelf cnn features"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "multi task cnn model for attribute prediction"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-7 (induced by source paper "generative adversarial models for people attribute recognition in surveillance"):
  with members:
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "contextual action recognition with r cnn"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "learning to recognize pedestrian attribute"

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)