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: "walk and learn facial attribute representation learning from egocentric video and contextual data"
  Abstract: The way people look in terms of facial attributes (ethnicity, hair color, facial hair, etc.) and the clothes or accessories they wear (sunglasses, hat, hoodies, etc.) is highly dependent on geo-location and weather condition, respectively. This work explores, for the first time, the use of this contextual information, as people with wearable cameras walk across different neighborhoods of a city, in order to learn a rich feature representation for facial attribute classification, without the costly manual annotation required by previous methods. By tracking the faces of casual walkers on more than 40 hours of egocentric video, we are able to cover tens of thousands of different identities and automatically extract nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations. These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features. Finally, the network is fine-tuned with manually annotated samples. We perform an extensive experimental analysis on wearable data and two standard benchmark datasets based on web images (LFWA and CelebA). Our method outperforms by a large margin a network trained from scratch. Moreover, even without using manually annotated identity labels for pre-training as in previous methods, our approach achieves results that are better than the state of the art.

Connected through hyperedge HE-1 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "transferability and hardness of supervised classification tasks"):
  with members:
    - "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"
    - "beyond sharing weights for deep domain adaptation"
    - "task2vec task embedding for meta learning"
    - "on measuring the iconicity of a face"
    - "the information complexity of learning tasks their structure and their distance"
    - "toward understanding catastrophic forgetting in continual learning"
    - "regularized learning for domain adaptation under label shifts"
    - "meta learning with latent embedding optimization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "aesthetic image captioning from weakly labelled photographs"):
  with members:
    - "colornet estimating colorfulness in natural images"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "a geometry sensitive approach for photographic style classification"
    - "learning to evaluate image captioning"
    - "neural algebra of classifiers"
    - "convolutional image captioning"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic assessment"

Connected through hyperedge HE-5 (induced by source paper "attend to count crowd counting with adaptive capacity multi scale cnns"):
  with members:
    - "dual path multi scale fusion networks with attention for crowd counting"
    - "point in box out beyond counting persons in crowds"
    - "crowd counting using scale aware attention networks"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "context aware crowd counting"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-6 (induced by source paper "sampling strategies for gan synthetic data"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "how good is my gan"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "deep facial expression recognition a survey"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-7 (induced by source paper "registration free face ssd single shot analysis of smiles facial attributes and affect in the wild"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "residual codean autoencoder for facial attribute analysis"
    - "single shot temporal action detection"
    - "single shot text detector with regional attention"
    - "scale aware face detection"
    - "simultaneous facial landmark detection pose and deformation estimation under facial occlusion"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

Connected through hyperedge HE-8 (induced by source paper "auglabel exploiting word representations to augment labels for face attribute classification"):
  with members:
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "improving facial attribute prediction using semantic segmentation"
    - "regularizing neural networks by penalizing confident output distributions"
    - "disturblabel regularizing cnn on the loss layer"
    - "cp mtml coupled projection multi task metric learning for large scale face retrieval"
    - "ask your neurons a deep learning approach to visual question answering"
    - "generative adversarial text to image synthesis"

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)