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: "crystal loss and quality pooling for unconstrained face verification and recognition"
  Abstract: In recent years, the performance of face verification and recognition systems based on deep convolutional neural networks (DCNNs) has significantly improved. A typical pipeline for face verification includes training a deep network for subject classification with softmax loss, using the penultimate layer output as the feature descriptor, and generating a cosine similarity score given a pair of face images or videos. The softmax loss function does not optimize the features to have higher similarity score for positive pairs and lower similarity score for negative pairs, which leads to a performance gap. In this paper, we propose a new loss function, called Crystal Loss, that restricts the features to lie on a hypersphere of a fixed radius. The loss can be easily implemented using existing deep learning frameworks. We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems. We achieve state-of-the-art performance for face verification and recognition on challenging LFW, IJB-A, IJB-B and IJB-C datasets over a large range of false alarm rates (10-1 to 10-7).

Connected through hyperedge HE-1 (induced by source paper "a quadruplet loss for enforcing semantically coherent embeddings in multi output classification problems"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "attention set based metric learning for video face recognition"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "ssd single shot multibox detector"
    - "convolutional neural networks for attribute based active authentication on mobile devices"
    - "facenet a unified embedding for face recognition and clustering"
    - "the megaface benchmark 1 million faces for recognition at scale"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-2 (induced by source paper "single unit status in deep convolutional neural network codes for face identification sparseness redefined"):
  with members:
    - "deep convolutional neural networks in the face of caricature identity and image revealed"
    - "how convolutional neural network see the world a survey of convolutional neural network visualization methods"
    - "the do s and don ts for cnn based face verification"
    - "triplet probabilistic embedding for face verification and clustering"
    - "an all in one convolutional neural network for face analysis"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "umdfaces an annotated face dataset for training deep networks"
    - "unconstrained face verification using deep cnn features"

Connected through hyperedge HE-3 (induced by source paper "adacos adaptively scaling cosine logits for effectively learning deep face representations"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"

Connected through hyperedge HE-4 (induced by source paper "recurrent embedding aggregation network for video face recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "multi prototype networks for unconstrained set based face recognition"
    - "spatial temporal recurrent neural network for emotion recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"

Connected through hyperedge HE-5 (induced by source paper "uncertainty modeling of contextual connection between tracklets for unconstrained video based face recognition"):
  with members:
    - "recurrent embedding aggregation network for video face recognition"
    - "video face recognition component wise feature aggregation network c fan"
    - "person search in videos with one portrait through visual and temporal links"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "an automatic system for unconstrained video based face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-6 (induced by source paper "p2sgrad refined gradients for optimizing deep face models"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-7 (induced by source paper "deep face recognition model compression via knowledge transfer and distillation"):
  with members:
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "p2sgrad refined gradients for optimizing deep face models"
    - "retinaface single stage dense face localisation in the wild"
    - "on measuring the iconicity of a face"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "l2 constrained softmax loss for discriminative face verification"
    - "systematic evaluation of convolution neural network advances on the imagenet"

Connected through hyperedge HE-8 (induced by source paper "video face recognition component wise feature aggregation network c fan"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "quality aware network for set to set recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "deepvisage making face recognition simple yet with powerful generalization skills"
    - "neural aggregation network for video 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)