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: "minimizing a sum of submodular functions"
  Abstract: We consider the problem of minimizing a function represented as a sum of submodular terms. We assume each term allows an efficient computation of {\em exchange capacities}. This holds, for example, for terms depending on a small number of variables, or for certain cardinality-dependent terms. #R##N#A naive application of submodular minimization algorithms would not exploit the existence of specialized exchange capacity subroutines for individual terms. To overcome this, we cast the problem as a {\em submodular flow} (SF) problem in an auxiliary graph, and show that applying most existing SF algorithms would rely only on these subroutines. #R##N#We then explore in more detail Iwata's capacity scaling approach for submodular flows (Math. Programming, 76(2):299--308, 1997). In particular, we show how to improve its complexity in the case when the function contains cardinality-dependent terms.

Connected through hyperedge HE-1 (induced by source paper "localized flow based clustering in hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "simplicial closure and higher order link prediction"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "flow based local graph clustering with better seed set inclusion"
    - "decomposable submodular function minimization discrete and continuous"

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"
    - "reflection methods for user friendly submodular optimization"
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"

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 "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"
    - "reflection methods for user friendly submodular optimization"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"

Connected through hyperedge HE-6 (induced by source paper "an efficient decomposition framework for discriminative segmentation with supermodular losses"):
  with members:
    - "provable submodular minimization using wolfe s algorithm"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"

Connected through hyperedge HE-7 (induced by source paper "subquadratic submodular function minimization"):
  with members:
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "tight bounds for approximate carath eodory and beyond"
    - "provable submodular minimization using wolfe s algorithm"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "reflection methods for user friendly submodular optimization"

Connected through hyperedge HE-8 (induced by source paper "complexity of discrete energy minimization problems"):
  with members:
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "the power of linear programming for valued csps"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"
    - "curvature prior for mrf based segmentation and shape inpainting"
    - "the expressive power of binary 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.DS(Data Structures and Algorithms)