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: "sspp dan deep domain adaptation network for face recognition with single sample per person"
  Abstract: Real-world face recognition using a single sample per person (SSPP) is a challenging task. The problem is exacerbated if the conditions under which the gallery image and the probe set are captured are completely different. To address these issues from the perspective of domain adaptation, we introduce an SSPP domain adaptation network (SSPP-DAN). In the proposed approach, domain adaptation, feature extraction, and classification are performed jointly using a deep architecture with domain-adversarial training. However, the SSPP characteristic of one training sample per class is insufficient to train the deep architecture. To overcome this shortage, we generate synthetic images with varying poses using a 3D face model. Experimental evaluations using a realistic SSPP dataset show that deep domain adaptation and image synthesis complement each other and dramatically improve accuracy. Experiments on a benchmark dataset using the proposed approach show state-of-the-art performance. All the dataset and the source code can be found in our online repository (this https URL).

Connected through hyperedge HE-1 (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"
    - "unsupervised domain adaptation for face recognition in unlabeled videos"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-2 (induced by source paper "collaborative training of balanced random forests for open set domain adaptation"):
  with members:
    - "weakly supervised open set domain adaptation by dual domain collaboration"
    - "domain symmetric networks for adversarial domain adaptation"
    - "algorithms and theory for multiple source adaptation"
    - "open set domain adaptation by backpropagation"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "deep hashing network for unsupervised domain adaptation"
    - "partial transfer learning with selective adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "addressing appearance change in outdoor robotics with adversarial domain adaptation"):
  with members:
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "correlation alignment for unsupervised domain adaptation"
    - "what makes imagenet good for transfer learning"
    - "improved techniques for training gans"
    - "deep transfer learning with joint adaptation networks"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "content based video music retrieval using soft intra modal structure constraint"):
  with members:
    - "learning two branch neural networks for image text matching tasks"
    - "recognizing dynamic scenes with deep dual descriptor based on key frames and key segments"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "image text multi modal representation learning by adversarial backpropagation"
    - "youtube 8m a large scale video classification benchmark"
    - "deep pyramidal residual networks"
    - "convolutional recurrent neural networks for music classification"
    - "learning deep structure preserving image text embeddings"

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)