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: "unsupervised domain adaptation for face recognition in unlabeled videos"
  Abstract: Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vast difference in visual quality between the domains and the difficulty of curating diverse large-scale video datasets. This paper addresses both of those challenges, through an image to video feature-level domain adaptation approach, to learn discriminative video frame representations. The framework utilizes large-scale unlabeled video data to reduce the gap between different domains while transferring discriminative knowledge from large-scale labeled still images. Given a face recognition network that is pretrained in the image domain, the adaptation is achieved by (i) distilling knowledge from the network to a video adaptation network through feature matching, (ii) performing feature restoration through synthetic data augmentation and (iii) learning a domain-invariant feature through a domain adversarial discriminator. We further improve performance through a discriminator-guided feature fusion that boosts high-quality frames while eliminating those degraded by video domain-specific factors. Experiments on the YouTube Faces and IJB-A datasets demonstrate that each module contributes to our feature-level domain adaptation framework and substantially improves video face recognition performance to achieve state-of-the-art accuracy. We demonstrate qualitatively that the network learns to suppress diverse artifacts in videos such as pose, illumination or occlusion without being explicitly trained for them.

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"
    - "probabilistic face embeddings"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "multi modal domain adaptation for fine grained action recognition"):
  with members:
    - "temporal attentive alignment for large scale video domain adaptation"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "domain generalization by solving jigsaw puzzles"
    - "unsupervised domain adaptation through self supervision"
    - "adversarial cross domain action recognition with co attention"
    - "scaling egocentric vision the epic kitchens dataset"
    - "slowfast networks for video recognition"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-3 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition"):
  with members:
    - "m adda unsupervised domain adaptation with deep metric learning"
    - "self supervised learning of face representations for video face clustering"
    - "domain specific face synthesis for video face recognition from a single sample per person"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sspp dan deep domain adaptation network for face recognition with single sample per person"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation for structured output via discriminative patch representations"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "domain adaptation for semantic segmentation via class balanced self training"
    - "image to image translation for domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "adaptation across extreme variations using unlabeled domain bridges"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "multi adversarial domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"
    - "adversarial discriminative domain adaptation"
    - "multiple source domain adaptation with adversarial training of neural networks"

Connected through hyperedge HE-6 (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"
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "ghostvlad for set based face recognition"
    - "multicolumn networks for face recognition"

Connected through hyperedge HE-7 (induced by source paper "gotta adapt em all joint pixel and feature level domain adaptation for recognition in the wild"):
  with members:
    - "towards high fidelity nonlinear 3d face morphable model"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "nonlinear 3d face morphable model"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "good semi supervised learning that requires a bad gan"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "racial faces in the wild reducing racial bias by information maximization adaptation network"):
  with members:
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "deep visual domain adaptation a survey"
    - "arcface additive angular margin loss for deep face recognition"
    - "supervised cosmos autoencoder learning beyond the euclidean loss"
    - "deep face recognition a survey"
    - "gender privacy an ensemble of semi adversarial networks for confounding arbitrary gender classifiers"
    - "progressive feature alignment for unsupervised domain adaptation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"

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)