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: "cheeger inequalities for submodular transformations"
  Abstract: The Cheeger inequality for undirected graphs, which relates the conductance of an undirected graph and the second smallest eigenvalue of its normalized Laplacian, is a cornerstone of spectral graph theory. The Cheeger inequality has been extended to directed graphs and hypergraphs using normalized Laplacians for those, that are no longer linear but piecewise linear transformations. #R##N#In this paper, we introduce the notion of a submodular transformation $F:\{0,1\}^n \to \mathbb{R}^m$, which applies $m$ submodular functions to the $n$-dimensional input vector, and then introduce the notions of its Laplacian and normalized Laplacian. With these notions, we unify and generalize the existing Cheeger inequalities by showing a Cheeger inequality for submodular transformations, which relates the conductance of a submodular transformation and the smallest non-trivial eigenvalue of its normalized Laplacian. This result recovers the Cheeger inequalities for undirected graphs, directed graphs, and hypergraphs, and derives novel Cheeger inequalities for mutual information and directed information. #R##N#Computing the smallest non-trivial eigenvalue of a normalized Laplacian of a submodular transformation is NP-hard under the small set expansion hypothesis. In this paper, we present a polynomial-time $O(\log n)$-approximation algorithm for the symmetric case, which is tight, and a polynomial-time $O(\log^2n+\log n \cdot \log m)$-approximation algorithm for the general case. #R##N#We expect the algebra concerned with submodular transformations, or \emph{submodular algebra}, to be useful in the future not only for generalizing spectral graph theory but also for analyzing other problems that involve piecewise linear transformations, e.g., deep learning.

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 "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"
    - "computing minimum cuts in hypergraphs"
    - "higher order organization of complex networks"

Connected through hyperedge HE-3 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "sketching cuts in graphs and hypergraphs"
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "representation approximation and learning of submodular functions using low rank decision trees"
    - "learning pseudo boolean k dnf and submodular functions"
    - "submodular functions are noise stable"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "privately releasing conjunctions and the statistical query barrier"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-4 (induced by source paper "quadratic decomposable submodular function minimization"):
  with members:
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "inhomogoenous hypergraph clustering with applications"
    - "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"
    - "reflection methods for user friendly submodular optimization"
    - "learning by transduction"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-5 (induced by source paper "finding cheeger cuts in hypergraphs via heat equation"):
  with members:
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "diffusion operator and spectral analysis for directed hypergraph laplacian"
    - "heat kernel based community detection"
    - "reductions between expansion problems"

Connected through hyperedge HE-6 (induced by source paper "polynomial time algorithms for submodular laplacian systems"):
  with members:
    - "hypergraph markov operators eigenvalues and approximation algorithms"
    - "convex analysis and optimization with submodular functions a tutorial"
    - "faster generation of random spanning trees"

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)