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: "domain adaptation for ear recognition using deep convolutional neural networks"
  Abstract: Here, the authors have extensively investigated the unconstrained ear recognition problem. The authors have first shown the importance of domain adaptation, when deep convolutional neural network (CNN) models are used for ear recognition. To enable domain adaptation, the authors have collected a new ear data set using the Multi-PIE face data set, which they named as Multi-PIE ear data set. The authors have analysed in depth the effect of ear image quality, for example, illumination and aspect ratio, on the classification performance. Finally, the authors have addressed the problem of data set bias in the ear recognition field. Experiments on the UERC data set have shown that domain adaptation leads to a significant performance improvement. For example, when VGG-16 model is used and the domain adaptation is applied, an absolute increase of around 10% has been achieved. Combining different deep CNN models has further improved the accuracy by 4%. In the experiments that the authors have conducted to examine the data set bias, given an ear image, they were able to classify the data set that it has come from with 99.71% accuracy, which indicates a strong bias among the ear recognition data sets.

Connected through hyperedge HE-1 (induced by source paper "semi supervised representation learning via dual autoencoders for domain adaptation"):
  with members:
    - "a survey on deep transfer learning"
    - "partial adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "an extended framework for marginalized domain adaptation"
    - "conditional adversarial domain adaptation"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "return of frustratingly easy domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "biometric recognition using deep learning a survey"):
  with members:
    - "p2sgrad refined gradients for optimizing deep face models"
    - "thirdeye triplet based iris recognition without normalization"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "deepiris iris recognition using a deep learning approach"
    - "fingernet pushing the limits of fingerprint recognition using convolutional neural network"
    - "generating talking face landmarks from speech"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "multimodal age and gender classification using ear and profile face images"):
  with members:
    - "age and gender classification from ear images"
    - "fine grained age estimation in the wild with attention lstm networks"
    - "ear recognition more than a survey"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-4 (induced by source paper "the unconstrained ear recognition challenge 2019 arxiv version with appendix"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "employing fusion of learned and handcrafted features for unconstrained ear recognition"
    - "training convolutional neural networks with limited training data for ear recognition in the wild"
    - "ear recognition more than a survey"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "age and gender classification from ear images"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the unconstrained ear recognition challenge"
    - "ear recognition more than a survey"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)