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: "constrained deep transfer feature learning and its applications"
  Abstract: Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for some application domains. Transfer learning can be one of the approaches to alleviate this problem by transferring data from data-rich source domain to data-scarce target domain. Existing transfer learning methods typically perform one-shot transfer learning and often ignore the specific properties that the transferred data must satisfy. To address these issues, we introduce a constrained deep transfer feature learning method to perform simultaneous transfer learning and feature learning by performing transfer learning in a progressively improving feature space iteratively in order to better narrow the gap between the target domain and the source domain for effective transfer of the data from the source domain to target domain. Furthermore, we propose to exploit the target domain knowledge and incorporate such prior knowledge as a constraint during transfer learning to ensure that the transferred data satisfies certain properties of the target domain. To demonstrate the effectiveness of the proposed constrained deep transfer feature learning method, we apply it to thermal feature learning for eye detection by transferring from the visible domain. We also applied the proposed method for cross-view facial expression recognition as a second application. The experimental results demonstrate the effectiveness of the proposed method for both applications.

Connected through hyperedge HE-1 (induced by source paper "truly generalizable radiograph segmentation with conditional domain adaptation"):
  with members:
    - "a curriculum domain adaptation approach to the semantic segmentation of urban scenes"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "deep visual domain adaptation a survey"
    - "multimodal unsupervised image to image translation"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "image to image translation for domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "non iterative knowledge fusion in deep convolutional neural networks"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-3 (induced by source paper "deep nearest class mean model for incremental odor classification"):
  with members:
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "a character level decoder without explicit segmentation for neural machine translation"
    - "domain adaptation extreme learning machines for drift compensation in e nose systems"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "sequence to sequence learning with neural networks"

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)