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: "random coordinate descent methods for minimizing decomposable submodular functions"
  Abstract: Submodular function minimization is a fundamental optimization problem that arises in several applications in machine learning and computer vision. The problem is known to be solvable in polynomial time, but general purpose algorithms have high running times and are unsuitable for large-scale problems. Recent work have used convex optimization techniques to obtain very practical algorithms for minimizing functions that are sums of ``simple" functions. In this paper, we use random coordinate descent methods to obtain algorithms with faster linear convergence rates and cheaper iteration costs. Compared to alternating projection methods, our algorithms do not rely on full-dimensional vector operations and they converge in significantly fewer iterations.

Connected through hyperedge HE-1 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "distributed and streaming linear programming in low dimensions"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"):
  with members:
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "truly sub cubic algorithms for language edit distance and rna folding via fast bounded difference min plus product"
    - "powers of tensors and fast matrix multiplication"
    - "parallel algorithms for geometric graph problems"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "exact weight subgraphs and the k sum conjecture"
    - "communication steps for parallel query processing"
    - "faster algorithms for rectangular matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "fast decomposable submodular function minimization using constrained total variation"):
  with members:
    - "decomposable submodular function minimization discrete and continuous"
    - "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"
    - "reflection methods for user friendly submodular optimization"
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"
    - "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 "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"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-7 (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"
    - "provable submodular minimization using wolfe s algorithm"
    - "reflection methods for user friendly submodular optimization"
    - "learning by transduction"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-8 (induced by source paper "decomposable submodular function minimization discrete and continuous"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "structured learning of sum of submodular higher order energy functions"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "minimizing a sum of submodular functions"

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)