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: "probabilistic face embeddings"
  Abstract: Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space. However, in a fully unconstrained face setting, the facial features learned by the embedding model could be ambiguous or may not even be present in the input face, leading to noisy representations. We propose Probabilistic Face Embeddings (PFEs), which represent each face image as a Gaussian distribution in the latent space. The mean of the distribution estimates the most likely feature values while the variance shows the uncertainty in the feature values. Probabilistic solutions can then be naturally derived for matching and fusing PFEs using the uncertainty information. Empirical evaluation on different baseline models, training datasets and benchmarks show that the proposed method can improve the face recognition performance of deterministic embeddings by converting them into PFEs. The uncertainties estimated by PFEs also serve as good indicators of the potential matching accuracy, which are important for a risk-controlled recognition system.

Connected through hyperedge HE-1 (induced by source paper "generalizing face representation with unlabeled data"):
  with members:
    - "circle loss a unified perspective of pair similarity optimization"
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "domain generalization by solving jigsaw puzzles"
    - "retinaface single stage dense face localisation in the wild"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "towards universal representation learning for deep face recognition"):
  with members:
    - "towards universal object detection by domain attention"
    - "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"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-3 (induced by source paper "how does gender balance in training data affect face recognition accuracy"):
  with members:
    - "analysis of gender inequality in face recognition accuracy"
    - "does face recognition accuracy get better with age deep face matchers say no"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "characterizing the variability in face recognition accuracy relative to race"
    - "retinaface single stage dense face localisation in the wild"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-4 (induced by source paper "debface de biasing face recognition"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "generative adversarial minority oversampling"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"
    - "minimax entropy network learning category invariant features for domain adaptation"
    - "class balanced loss based on effective number of samples"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multi task convolutional neural network for pose invariant 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)