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: "improving facial attribute prediction using semantic segmentation"
  Abstract: Attributes are semantically meaningful characteristics whose applicability widely crosses category boundaries. They are particularly important in describing and recognizing concepts where no explicit training example is given, \textit{e.g., zero-shot learning}. Additionally, since attributes are human describable, they can be used for efficient human-computer interaction. In this paper, we propose to employ semantic segmentation to improve facial attribute prediction. The core idea lies in the fact that many facial attributes describe local properties. In other words, the probability of an attribute to appear in a face image is far from being uniform in the spatial domain. We build our facial attribute prediction model jointly with a deep semantic segmentation network. This harnesses the localization cues learned by the semantic segmentation to guide the attention of the attribute prediction to the regions where different attributes naturally show up. As a result of this approach, in addition to recognition, we are able to localize the attributes, despite merely having access to image level labels (weak supervision) during training. We evaluate our proposed method on CelebA and LFWA datasets and achieve superior results to the prior arts. Furthermore, we show that in the reverse problem, semantic face parsing improves when facial attributes are available. That reaffirms the need to jointly model these two interconnected tasks.

Connected through hyperedge HE-1 (induced by source paper "boosting deep face recognition via disentangling appearance and geometry"):
  with members:
    - "robust facial landmark detection via aggregation on geometrically manipulated faces"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "fast geometrically perturbed adversarial faces"
    - "a deep face identification network enhanced by facial attributes prediction"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"

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"
    - "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"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "learning about an exponential amount of conditional distributions"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "unsupervised representation learning by predicting image rotations"
    - "multi task self supervised visual learning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "a universal marginalizer for amortized inference in generative models"
    - "curiosity driven exploration by self supervised prediction"
    - "deep variational information bottleneck"
    - "ambient sound provides supervision for visual learning"

Connected through hyperedge HE-5 (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"
    - "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"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "ask your neurons a deep learning approach to visual question answering"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-6 (induced by source paper "deep imbalanced learning for face recognition and attribute prediction"):
  with members:
    - "learning towards minimum hyperspherical energy"
    - "learning to reweight examples for robust deep learning"
    - "feature transfer learning for deep face recognition with under represented data"
    - "sphereface deep hypersphere embedding for face recognition"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "one shot face recognition by promoting underrepresented classes"
    - "class rectification hard mining for imbalanced deep learning"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "part level car parsing and reconstruction from single street view"):
  with members:
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "instance level human parsing via part grouping network"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "3d pose estimation for fine grained object categories"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "adversarial discriminative domain adaptation"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-8 (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"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

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)