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: "multi label learning based deep transfer neural network for facial attribute classification"
  Abstract: Abstract   Deep Neural Network (DNN) has recently achieved outstanding performance in a variety of computer vision tasks, including facial attribute classification. The great success of classifying facial attributes with DNN often relies on a massive amount of labelled data. However, in real-world applications, labelled data are only provided for some commonly used attributes (such as age, gender); whereas, unlabelled data are available for other attributes (such as attraction, hairline). To address the above problem, we propose a novel deep transfer neural network method based on multi-label learning for facial attribute classification, termed FMTNet, which consists of three sub-networks: the Face detection Network (FNet), the Multi-label learning Network (MNet) and the Transfer learning Network (TNet). Firstly, based on the Faster Region-based Convolutional Neural Network (Faster R-CNN), FNet is fine-tuned for face detection. Then, MNet is fine-tuned by FNet to predict multiple attributes with labelled data, where an effective loss weight scheme is developed to explicitly exploit the correlation between facial attributes based on attribute grouping. Finally, based on MNet, TNet is trained by taking advantage of unsupervised domain adaptation for unlabelled facial attribute classification. The three sub-networks are tightly coupled to perform effective facial attribute classification. A distinguishing characteristic of the proposed FMTNet method is that the three sub-networks (FNet, MNet and TNet) are constructed in a similar network structure. Extensive experimental results on challenging face datasets demonstrate the effectiveness of our proposed method compared with several state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "mobilefan transferring deep hidden representation for face alignment"):
  with members:
    - "structured knowledge distillation for dense prediction"
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "simple baselines for human pose estimation and tracking"
    - "improving fast segmentation with teacher student learning"
    - "style aggregated network for facial landmark detection"

Connected through hyperedge HE-2 (induced by source paper "generalization of feature embeddings transferred from different video anomaly detection domains"):
  with members:
    - "unsupervised representation learning using convolutional and stacked auto encoders a domain and cross domain feature space analysis"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "providing theoretical learning guarantees to deep learning networks"
    - "learning temporal regularity in video sequences"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "cascademl an automatic neural network architecture evolution and training algorithm for multi label classification"):
  with members:
    - "automated multi label classification based on ml plan"
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep learning for multi label classification"

Connected through hyperedge HE-4 (induced by source paper "adversarial information factorization"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "face aging with conditional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "cvae gan fine grained image generation through asymmetric training"
    - "unsupervised cross domain image generation"
    - "improved techniques for training gans"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (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"
    - "attributes for improved attributes a multi task network for 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"

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)