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: "generalized multi view embedding for visual recognition and cross modal retrieval"
  Abstract: In this paper, the problem of multi-view embedding from different visual cues and modalities is considered. We propose a unified solution for subspace learning methods using the Rayleigh quotient, which is extensible for multiple views, supervised learning, and nonlinear embeddings. Numerous methods including canonical correlation analysis, partial least square regression, and linear discriminant analysis are studied using specific intrinsic and penalty graphs within the same framework. Nonlinear extensions based on kernels and (deep) neural networks are derived, achieving better performance than the linear ones. Moreover, a novel multi-view modular discriminant analysis is proposed by taking the view difference into consideration. We demonstrate the effectiveness of the proposed multi-view embedding methods on visual object recognition and cross-modal image retrieval, and obtain superior results in both applications compared to related methods.

Connected through hyperedge HE-1 (induced by source paper "deep multi view learning to rank"):
  with members:
    - "deep multimodal subspace clustering networks"
    - "correlational neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of words and phrases and their compositionality"
    - "a multi view embedding space for modeling internet images tags and their semantics"
    - "learning to order things"

Connected through hyperedge HE-2 (induced by source paper "speed up and multi view extensions to subclass discriminant analysis"):
  with members:
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"

Connected through hyperedge HE-3 (induced by source paper "multi view common component discriminant analysis for cross view classification"):
  with members:
    - "multi view intact space learning"
    - "multi view hybrid embedding a divide and conquer approach"
    - "adversarial discriminative domain adaptation"
    - "multichannel kernel canonical correlation analysis for cross view person reidentification"
    - "person re identification by unsupervised video matching"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-4 (induced by source paper "domain adaptation for visual applications a comprehensive survey"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "adversarial discriminative domain adaptation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "play and learn using video games to train computer vision models"
    - "deep transfer learning with joint adaptation networks"
    - "lightweight unsupervised domain adaptation by convolutional filter reconstruction"
    - "coupled generative adversarial 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)