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: "hierarchical clustering with structural constraints"
  Abstract: Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm. This gives rise to the problem of "hierarchical clustering with structural constraints". Structural constraints pose major challenges for bottom-up approaches like average/single linkage and even though they can be naturally incorporated into top-down divisive algorithms, no formal guarantees exist on the quality of their output. In this paper, we provide provable approximation guarantees for two simple top-down algorithms, using a recently introduced optimization viewpoint of hierarchical clustering with pairwise similarity information [Dasgupta, 2016]. We show how to find good solutions even in the presence of conflicting prior information, by formulating a constraint-based regularization of the objective. We further explore a variation of this objective for dissimilarity information [Cohen-Addad et al., 2018] and improve upon current techniques. Finally, we demonstrate our approach on a real dataset for the taxonomy application.

Connected through hyperedge HE-1 (induced by source paper "deep discriminative clustering analysis"):
  with members:
    - "variational wasserstein clustering"
    - "deep clustering for unsupervised learning of visual features"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep continuous clustering"
    - "relational inductive biases deep learning and graph networks"
    - "learning markov clustering networks for scene text detection"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "learning from video and text via large scale discriminative clustering"

Connected through hyperedge HE-2 (induced by source paper "bisect and conquer hierarchical clustering via max uncut bisection"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering for euclidean data"
    - "adaptive hierarchical clustering using ordinal queries"
    - "hierarchical clustering objective functions and algorithms"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"

Connected through hyperedge HE-3 (induced by source paper "ultrametric fitting by gradient descent"):
  with members:
    - "on the convergence of adam and beyond"
    - "reliable agglomerative clustering"
    - "hierarchical graph clustering using node pair sampling"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "interactive bayesian hierarchical clustering"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"

Connected through hyperedge HE-4 (induced by source paper "deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "clustering driven deep embedding with pairwise 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-5 (induced by source paper "a framework for deep constrained clustering algorithms and advances"):
  with members:
    - "probabilistic formulations of regression with mixed guidance"
    - "clustering driven deep embedding with pairwise 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-6 (induced by source paper "supervised hierarchical clustering with exponential linkage"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "resolving event coreference with supervised representation learning and clustering oriented regularization"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "hierarchical clustering better than average linkage"):
  with members:
    - "hierarchical clustering via spreading metrics"

Connected through hyperedge HE-8 (induced by source paper "hierarchical clustering objective functions and algorithms"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "on the practically interesting instances of maxcut"
    - "a uniqueness theorem for clustering"
    - "improved spectral norm bounds for clustering"
    - "efficient active algorithms for hierarchical clustering"

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.DS(Data Structures and Algorithms)