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: "pose robust face recognition via deep residual equivariant mapping"
  Abstract: Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces. A key reason is that the number of frontal and profile training faces are highly imbalanced - there are extensively more frontal training samples compared to profile ones. In addition, it is intrinsically hard to learn a deep representation that is geometrically invariant to large pose variations. In this study, we hypothesize that there is an inherent mapping between frontal and profile faces, and consequently, their discrepancy in the deep representation space can be bridged by an equivariant mapping. To exploit this mapping, we formulate a novel Deep Residual EquivAriant Mapping (DREAM) block, which is capable of adaptively adding residuals to the input deep representation to transform a profile face representation to a canonical pose that simplifies recognition. The DREAM block consistently enhances the performance of profile face recognition for many strong deep networks, including ResNet models, without deliberately augmenting training data of profile faces. The block is easy to use, light-weight, and can be implemented with a negligible computational overhead.

Connected through hyperedge HE-1 (induced by source paper "distribution distillation loss generic approach for improving face recognition from hard samples"):
  with members:
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "additive margin softmax for face verification"
    - "support vector guided softmax loss for face recognition"
    - "comparator networks"
    - "normface l 2 hypersphere embedding for face verification"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "dotfan a domain transferred face augmentation network for pose and illumination invariant face recognition"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "synthesizing normalized faces from facial identity features"

Connected through hyperedge HE-3 (induced by source paper "learning imbalanced datasets with label distribution aware margin loss"):
  with members:
    - "diversity in faces"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "push the student to learn right progressive gradient correcting by meta learner on corrupted labels"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "overfitting of neural nets under class imbalance analysis and improvements for segmentation"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "regularized learning for domain adaptation under label shifts"

Connected through hyperedge HE-4 (induced by source paper "decorrelated adversarial learning for age invariant face recognition"):
  with members:
    - "orthogonal deep features decomposition for age invariant face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "super identity convolutional neural network for face hallucination"
    - "exploring disentangled feature representation beyond face identification"
    - "additive margin softmax for face verification"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-5 (induced by source paper "attentional feature pair relation networks for accurate face recognition"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "pairwise relational networks for face recognition"
    - "comparator networks"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "beyond bilinear generalized multi modal factorized high order pooling for visual question answering"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"

Connected through hyperedge HE-6 (induced by source paper "facial pose estimation by deep learning from label distributions"):
  with members:
    - "a realistic face to face conversation system based on deep neural networks"
    - "video synthesis of human upper body with realistic face"
    - "a neural virtual anchor synthesizer based on seq2seq and gan models"
    - "detecting gaze towards eyes in natural social interactions and its use in child assessment"
    - "fine grained head pose estimation without keypoints"
    - "deep label distribution learning with label ambiguity"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"

Connected through hyperedge HE-7 (induced by source paper "face detection and face recognition in the wild using off the shelf freely available components"):
  with members:
    - "do we really need to collect millions of faces for effective face recognition"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "face recognition using deep multi pose representations"
    - "a deep pyramid deformable part model for face detection"

Connected through hyperedge HE-8 (induced by source paper "towards interpretable face recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "gait recognition via disentangled representation learning"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks 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)