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: "on constrained spectral clustering and its applications"
  Abstract: Constrained clustering has been well-studied for algorithms such as K-means and hierarchical clustering. However, how to satisfy many constraints in these algorithmic settings has been shown to be intractable. One alternative to encode many constraints is to use spectral clustering, which remains a developing area. In this paper, we propose a flexible framework for constrained spectral clustering. In contrast to some previous efforts that implicitly encode Must-Link (ML) and Cannot-Link (CL) constraints by modifying the graph Laplacian or constraining the underlying eigenspace, we present a more natural and principled formulation, which explicitly encodes the constraints as part of a constrained optimization problem. Our method offers several practical advantages: it can encode the degree of belief in ML and CL constraints; it guarantees to lower-bound how well the given constraints are satisfied using a user-specified threshold; it can be solved deterministically in polynomial time through generalized eigendecomposition. Furthermore, by inheriting the objective function from spectral clustering and encoding the constraints explicitly, much of the existing analysis of unconstrained spectral clustering techniques remains valid for our formulation. We validate the effectiveness of our approach by empirical results on both artificial and real datasets. We also demonstrate an innovative use of encoding large number of constraints: transfer learning via constraints.

Connected through hyperedge HE-1 (induced by source paper "deep amortized clustering"):
  with members:
    - "multi class classification without multi class labels"
    - "are few shot learning benchmarks too simple"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "clustering with deep learning taxonomy and new methods"
    - "conditional neural processes"
    - "deep sets"
    - "invariant information clustering for unsupervised image classification and segmentation"
    - "user experience of the costar system for instruction of collaborative robots"

Connected through hyperedge HE-2 (induced by source paper "multi class classification without multi class labels"):
  with members:
    - "classification from pairwise similarity and unlabeled data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "visualizing the loss landscape of neural nets"
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-3 (induced by source paper "robust motion segmentation from pairwise matches"):
  with members:
    - "motion segmentation by exploiting complementary geometric models"
    - "multi class model fitting by energy minimization and mode seeking"
    - "a survey of structure from motion"
    - "spectral synchronization of multiple views in se 3"
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic constrained clustering for transfer learning and image category discovery"):
  with members:
    - "learning to cluster in order to transfer across domains and tasks"
    - "large scale object discovery and detector adaptation from unlabeled video"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "deep image category discovery using a transferred similarity function"
    - "neural network based clustering using pairwise constraints"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "motion segmentation by exploiting complementary geometric models"):
  with members:
    - "shape interaction matrix revisited and robustified efficient subspace clustering with corrupted and incomplete data"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "learning concept embeddings with combined human machine expertise"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "efficient online relative comparison kernel learning"
    - "going deeper with convolutions"
    - "bird species categorization using pose normalized deep convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning fine grained image similarity with deep ranking"
    - "cost effective hits for relative similarity comparisons"

Connected through hyperedge HE-7 (induced by source paper "scalable constrained clustering a generalized spectral method"):
  with members:
    - "a generalized cheeger inequality"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "social structure of facebook networks"
    - "a tutorial on spectral clustering"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "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.LG(Machine Learning)