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: "reflection methods for user friendly submodular optimization"
  Abstract: Recently, it has become evident that submodularity naturally captures widely occurring concepts in machine learning, signal processing and computer vision. Consequently, there is need for efficient optimization procedures for submodular functions, especially for minimization problems. While general submodular minimization is challenging, we propose a new method that exploits existing decomposability of submodular functions. In contrast to previous approaches, our method is neither approximate, nor impractical, nor does it need any cumbersome parameter tuning. Moreover, it is easy to implement and parallelize. A key component of our method is a formulation of the discrete submodular minimization problem as a continuous best approximation problem that is solved through a sequence of reflections, and its solution can be easily thresholded to obtain an optimal discrete solution. This method solves both the continuous and discrete formulations of the problem, and therefore has applications in learning, inference, and reconstruction. In our experiments, we illustrate the benefits of our method on two image segmentation tasks.

Connected through hyperedge HE-1 (induced by source paper "quadratic decomposable submodular function minimization theory and practice"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "motif and hypergraph correlation clustering"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "greedy algorithms for cone constrained optimization with convergence guarantees"
    - "inhomogoenous hypergraph clustering with applications"

Connected through hyperedge HE-2 (induced by source paper "fast decomposable submodular function minimization using constrained total variation"):
  with members:
    - "decomposable submodular function minimization discrete and continuous"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "submodular functions from discrete to continous domains"
    - "convex optimization for parallel energy minimization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "provable submodular minimization using wolfe s algorithm"
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-3 (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-4 (induced by source paper "revisiting decomposable submodular function minimization with incidence relations"):
  with members:
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "inhomogoenous hypergraph clustering with applications"
    - "computing minimum cuts in hypergraphs"
    - "semi supervised classification with graph convolutional networks"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-5 (induced by source paper "quadratic decomposable submodular function minimization"):
  with members:
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "diffusion operator and spectral analysis for directed hypergraph laplacian"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "provable submodular minimization using wolfe s algorithm"
    - "learning by transduction"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-6 (induced by source paper "distributed submodular minimization over networks a greedy column generation approach"):
  with members:
    - "distributed submodular maximization with limited information"
    - "a supermodular optimization framework for leader selection under link noise in linear multi agent systems"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "structured sparsity inducing norms through submodular functions"
    - "distributed abstract optimization via constraints consensus theory and applications"

Connected through hyperedge HE-7 (induced by source paper "decomposable submodular function minimization discrete and continuous"):
  with members:
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "structured learning of sum of submodular higher order energy functions"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "minimizing a sum of submodular functions"

Connected through hyperedge HE-8 (induced by source paper "efficient algorithms for non convex isotonic regression through submodular optimization"):
  with members:
    - "submodular functions from discrete to continous domains"

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)