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: "cp mtml coupled projection multi task metric learning for large scale face retrieval"
  Abstract: We propose a novel Coupled Projection multi-task Metric Learning (CP-mtML) method for large scale face retrieval. In contrast to previous works which were limited to low dimensional features and small datasets, the proposed method scales to large datasets with high dimensional face descriptors. It utilises pairwise (dis-)similarity constraints as supervision and hence does not require exhaustive class annotation for every training image. While, traditionally, multi-task learning methods have been validated on same dataset but different tasks, we work on the more challenging setting with heterogeneous datasets and different tasks. We show empirical validation on multiple face image datasets of different facial traits, e.g. identity, age and expression. We use classic Local Binary Pattern (LBP) descriptors along with the recent Deep Convolutional Neural Network (CNN) features. The experiments clearly demonstrate the scalability and improved performance of the proposed method on the tasks of identity and age based face image retrieval compared to competitive existing methods, on the standard datasets and with the presence of a million distractor face images.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multi task driven feature models for thermal infrared tracking"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "learning deep multi level similarity for thermal infrared object tracking"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "target aware deep tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "a twofold siamese network for real time object tracking"
    - "vital visual tracking via adversarial learning"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "snap and find deep discrete cross domain garment image retrieval"):
  with members:
    - "automatic spatially aware fashion concept discovery"
    - "hashnet deep learning to hash by continuation"
    - "cross domain image retrieval with attention modeling"
    - "video captioning with transferred semantic attributes"
    - "a survey on learning to hash"
    - "boosting image captioning with attributes"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-5 (induced by source paper "gridface face rectification via learning local homography transformations"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "deep mean shift priors for image restoration"
    - "reconstruction based disentanglement for pose invariant face recognition"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "synthesizing normalized faces from facial identity features"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-6 (induced by source paper "large scale novel object discovery in 3d"):
  with members:
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "unsupervised learning of visual representations using videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "scalable nonlinear embeddings for semantic category based image retrieval"
    - "multimodal deep learning for robust rgb d object recognition"
    - "3d shapenets a deep representation for volumetric shapes"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "rotational projection statistics for 3d local surface description and object recognition"

Connected through hyperedge HE-7 (induced by source paper "improving speaker turn embedding by crossmodal transfer learning from face embedding"):
  with members:
    - "tristounet triplet loss for speaker turn embedding"
    - "look listen and learn a multimodal lstm for speaker identification"
    - "deep multimodal speaker naming"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning face representation from scratch"
    - "constrained speaker diarization of tv series based on visual patterns"

Connected through hyperedge HE-8 (induced by source paper "hard aware deeply cascaded embedding"):
  with members:
    - "branchynet fast inference via early exiting from deep neural networks"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "learning by tracking siamese cnn for robust target association"
    - "siamese instance search for tracking"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "pn net conjoined triple deep network for learning local image descriptors"
    - "local similarity aware deep feature embedding"
    - "learning deep embeddings with histogram loss"

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)