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: "context aware nonnegative matrix factorization clustering"
  Abstract: In this article we propose a method to refine the clustering results obtained with the nonnegative matrix factorization (NMF) technique, imposing consistency constraints on the final labeling of the data. The research community focused its effort on the initialization and on the optimization part of this method, without paying attention to the final cluster assignments. We propose a game theoretic framework in which each object to be clustered is represented as a player, which has to choose its cluster membership. The information obtained with NMF is used to initialize the strategy space of the players and a weighted graph is used to model the interactions among the players. These interactions allow the players to choose a cluster which is coherent with the clusters chosen by similar players, a property which is not guaranteed by NMF, since it produces a soft clustering of the data. The results on common benchmarks show that our model is able to improve the performances of many NMF formulations.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation using graph transduction games"):
  with members:
    - "ancient coin classification using graph transduction games"
    - "transductive label augmentation for improved deep network learning"
    - "associative domain adaptation"
    - "deep transfer learning with joint adaptation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "return of frustratingly easy domain adaptation"
    - "learning transferable features with deep adaptation networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "transductive label augmentation for improved deep network learning"):
  with members:
    - "densely connected convolutional networks"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "speaker clustering using dominant sets"):
  with members:
    - "voxceleb a large scale speaker identification dataset"
    - "dominant sets for constrained image segmentation"
    - "return of the devil in the details delving deep into convolutional nets"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

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)