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: "neural aggregation network for video face recognition"
  Abstract: This paper aims to learn a compact representation of a video for video face recognition task. We make the following contributions: first, we propose a meta attention-based aggregation scheme which adaptively and fine-grained weighs the feature along each feature dimension among all frames to form a compact and discriminative representation. It makes the best to exploit the valuable or discriminative part of each frame to promote the performance of face recognition, without discarding or despising low quality frames as usual methods do. Second, we build a feature aggregation network comprised of a feature embedding module and a feature aggregation module. The embedding module is a convolutional neural network used to extract a feature vector from a face image, while the aggregation module consists of cascaded two meta attention blocks which adaptively aggregate the feature vectors into a single fixed-length representation. The network can deal with arbitrary number of frames, and is insensitive to frame order. Third, we validate the performance of proposed aggregation scheme. Experiments on publicly available datasets, such as YouTube face dataset and IJB-A dataset, show the effectiveness of our method, and it achieves competitive performances on both the verification and identification protocols.

Connected through hyperedge HE-1 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "towards universal object detection by domain attention"
    - "probabilistic face embeddings"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "additive margin softmax for face verification"
    - "on calibration of modern neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"

Connected through hyperedge HE-2 (induced by source paper "learning to validate the quality of detected landmarks"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "style aggregated network for facial landmark detection"
    - "sphereface deep hypersphere embedding for face recognition"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep reinforcement learning a brief survey"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "deep reinforcement learning an overview"
    - "deep reinforcement learning attention selection for person re identification"
    - "classification with costly features using deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "adaptive wing loss for robust face alignment via heatmap regression"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "look at boundary a boundary aware face alignment algorithm"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "arcface additive angular margin loss for deep face recognition"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-5 (induced by source paper "probabilistic face embeddings"):
  with members:
    - "striking the right balance with uncertainty"
    - "video face recognition component wise feature aggregation network c fan"
    - "cosface large margin cosine loss for deep face recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards interpretable face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-6 (induced by source paper "region attention networks for pose and occlusion robust facial expression recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "frame attention networks for facial expression recognition in videos"
    - "emotion recognition in speech using cross modal transfer in the wild"
    - "deep facial expression recognition a survey"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "island loss for learning discriminative features in facial expression recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "multi prototype networks for unconstrained set based face recognition"):
  with members:
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "cosface large margin cosine loss for deep face recognition"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-8 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face 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)