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: "leveraging mid level deep representations for predicting face attributes in the wild"
  Abstract: Predicting facial attributes from faces in the wild is very challenging due to pose and lighting variations in the real world. The key to this problem is to build proper feature representations to cope with these unfavourable conditions. Given the success of Convolutional Neural Network (CNN) in image classification, the high-level CNN feature, as an intuitive and reasonable choice, has been widely utilized for this problem. In this paper, however, we consider the mid-level CNN features as an alternative to the high-level ones for attribute prediction. This is based on the observation that face attributes are different: some of them are locally oriented while others are globally defined. Our investigations reveal that the mid-level deep representations outperform the prediction accuracy achieved by the (fine-tuned) high-level abstractions. We empirically demonstrate that the midlevel representations achieve state-of-the-art prediction performance on CelebA and LFWA datasets. Our investigations also show that by utilizing the mid-level representations one can employ a single deep network to achieve both face recognition and attribute prediction.

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"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "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 "on symbiosis of attribute prediction and semantic segmentation"):
  with members:
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "improving facial attribute prediction using semantic segmentation"
    - "class rectification hard mining for imbalanced deep learning"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "rethinking atrous convolution for semantic image segmentation"
    - "semantic object parsing with graph lstm"

Connected through hyperedge HE-3 (induced by source paper "slim cnn a light weight cnn for face attribute prediction"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "supervised cosmos autoencoder learning beyond the euclidean loss"):
  with members:
    - "residual codean autoencoder for facial attribute analysis"
    - "semantic autoencoder for zero shot learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "relational autoencoder for feature extraction"
    - "improving facial attribute prediction using semantic segmentation"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"

Connected through hyperedge HE-5 (induced by source paper "residual codean autoencoder for facial attribute analysis"):
  with members:
    - "adversarial diversity and hard positive generation"
    - "face attribute prediction using off the shelf cnn features"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "affact alignment free facial attribute classification technique"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "multi label learning based deep transfer neural network for facial attribute classification"):
  with members:
    - "unsupervised domain adaptation with residual transfer networks"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "learning transferable features with deep adaptation networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "how transferable are features in deep neural networks"
    - "deep learning face representation by joint identification verification"
    - "deep learning face attributes in the wild"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-7 (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"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "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-8 (induced by source paper "attributes in multiple facial images"):
  with members:
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "deepid3 face recognition with very deep neural networks"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "naive deep face recognition touching the limit of lfw benchmark or not"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)