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: "an inverse power method for nonlinear eigenproblems with applications in 1 spectral clustering and sparse pca"
  Abstract: Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints. In this paper we show that a certain class of constrained optimization problems with nonquadratic objective and constraints can be understood as nonlinear eigenproblems. We derive a generalization of the inverse power method which is guaranteed to converge to a nonlinear eigenvector. We apply the inverse power method to 1-spectral clustering and sparse PCA which can naturally be formulated as nonlinear eigenproblems. In both applications we achieve state-of-the-art results in terms of solution quality and runtime. Moving beyond the standard eigenproblem should be useful also in many other applications and our inverse power method can be easily adapted to new problems.

Connected through hyperedge HE-1 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "smooth neighbors on teacher graphs for semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "tensor regression using low rank and sparse tucker decompositions"):
  with members:
    - "boosted sparse and low rank tensor regression"
    - "learning from multiway data simple and efficient tensor regression"
    - "low rank tensor recovery via iterative hard thresholding"
    - "lower bounds for sparse recovery"
    - "guaranteed rank minimization via singular value projection"
    - "most tensor problems are np hard"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "total variation based community detection using a nonlinear optimization approach"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "a nonlinear spectral method for core periphery detection in networks"
    - "community detection in political twitter networks using nonnegative matrix factorization methods"
    - "a method based on total variation for network modularity optimization using the mbo scheme"
    - "advanced coarsening schemes for graph partitioning"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-5 (induced by source paper "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "quadratic decomposable submodular function minimization"
    - "submodularity on hypergraphs from sets to sequences"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"

Connected through hyperedge HE-6 (induced by source paper "piecewise flat embedding for image segmentation"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "holistically nested edge detection"
    - "pushing the boundaries of boundary detection using deep learning"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-7 (induced by source paper "community detection in networks via nonlinear modularity eigenvectors"):
  with members:
    - "a nonlinear spectral method for core periphery detection in networks"

Connected through hyperedge HE-8 (induced by source paper "compassionately conservative balanced cuts for image segmentation"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"

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)