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: "clustering driven deep embedding with pairwise constraints"
  Abstract: Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network. We present a clustering-driven embedding based on a Siamese network that encourages pairs of data points to output similar representations in the latent space. Our pair-based model allows augmenting the information with labeled pairs to constitute a semi-supervised framework. Our approach is based on analyzing the losses associated with each pair to refine the set of constraints. We show that clustering performance increases when using this scheme, even with a limited amount of user queries. We demonstrate how our architecture is adapted for various types of data and present the first deep framework to cluster 3D shapes.

Connected through hyperedge HE-1 (induced by source paper "deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "hierarchical clustering with structural constraints"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "facenet a unified embedding for face recognition and clustering"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-2 (induced by source paper "a framework for deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "hierarchical clustering with structural constraints"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "facenet a unified embedding for face recognition and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "neural network based clustering using pairwise constraints"

Connected through hyperedge HE-3 (induced by source paper "clustering oriented representation learning with attractive repulsive loss"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "resolving event coreference with supervised representation learning and clustering oriented regularization"
    - "triplet center loss for multi view 3d object retrieval"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "low shot learning with imprinted weights"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "semi supervised clustering with neural networks"):
  with members:
    - "deep clustering via joint convolutional autoencoder embedding and relative entropy minimization"
    - "deep metric learning via facility location"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "joint unsupervised learning of deep representations and image clusters"
    - "unsupervised deep embedding for clustering analysis"
    - "neural network based clustering using pairwise constraints"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"

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)