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: "hypergraph markov operators eigenvalues and approximation algorithms"
  Abstract: The celebrated Cheeger's Inequality \cite{am85,a86} establishes a bound on the expansion of a graph via its spectrum. This inequality is central to a rich spectral theory of graphs, based on studying the eigenvalues and eigenvectors of the adjacency matrix (and other related matrices) of graphs. It has remained open to define a suitable spectral model for hypergraphs whose spectra can be used to estimate various combinatorial properties of the hypergraph. #R##N#In this paper we introduce a new hypergraph Laplacian operator (generalizing the Laplacian matrix of graphs)and study its spectra. We prove a Cheeger-type inequality for hypergraphs, relating the second smallest eigenvalue of this operator to the expansion of the hypergraph. We bound other hypergraph expansion parameters via higher eigenvalues of this operator. We give bounds on the diameter of the hypergraph as a function of the second smallest eigenvalue of the Laplacian operator. The Markov process underlying the Laplacian operator can be viewed as a dispersion process on the vertices of the hypergraph that might be of independent interest. We bound the {\em Mixing-time} of this process as a function of the second smallest eigenvalue of the Laplacian operator. All these results are generalizations of the corresponding results for graphs. #R##N#We show that there can be no linear operator for hypergraphs whose spectra captures hypergraph expansion in a Cheeger-like manner. For any $k$, we give a polynomial time algorithm to compute an approximation to the $k^{th}$ smallest eigenvalue of the operator. We show that this approximation factor is optimal under the SSE hypothesis (introduced by \cite{rs10}) for constant values of $k$. #R##N#Finally, using the factor preserving reduction from vertex expansion in graphs to hypergraph expansion, we show that all our results for hypergraphs extend to vertex expansion in graphs.

Connected through hyperedge HE-1 (induced by source paper "hypergcn a new method of training graph convolutional networks on hypergraphs"):
  with members:
    - "graph colouring meets deep learning effective graph neural network models for combinatorial problems"
    - "exact k recommendation via maximal clique optimization"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "multi label image recognition with graph convolutional networks"
    - "approximate k cover in hypergraphs efficient algorithms and applications"
    - "planted hitting set recovery in hypergraphs"
    - "a comprehensive survey on graph neural networks"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

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"
    - "cheeger inequalities for submodular transformations"
    - "computing minimum cuts in hypergraphs"

Connected through hyperedge HE-3 (induced by source paper "spectral sparsification of hypergraphs"):
  with members:
    - "cheeger inequalities for submodular transformations"
    - "sketching cuts in graphs and hypergraphs"
    - "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 "generalizing the hypergraph laplacian via a diffusion process with mediators"):
  with members:
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "correlation clustering with noisy partial information"
    - "partitioning well clustered graphs spectral clustering works"
    - "approximation algorithm for sparsest k partitioning"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"

Connected through hyperedge HE-5 (induced by source paper "curvature of hypergraphs via multi marginal optimal transport"):
  with members:
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "higher order organization of complex networks"
    - "spectral properties of laplacian and stochastic diffusion process for edge expansion in hypergraphs"
    - "ricci curvature of the internet topology"

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

Connected through hyperedge HE-7 (induced by source paper "cheeger inequalities for submodular transformations"):
  with members:
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "polynomial time algorithms for submodular laplacian systems"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "inhomogoenous hypergraph clustering with applications"
    - "spectral properties of hypergraph laplacian and approximation algorithms"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "provable submodular minimization using wolfe s algorithm"

Connected through hyperedge HE-8 (induced by source paper "inhomogoenous hypergraph clustering with applications"):
  with members:
    - "on spectral partitioning of signed graphs"
    - "higher order organization of complex networks"
    - "estimation of kl divergence optimal minimax rate"
    - "scalable motif aware graph clustering"
    - "motif clustering and overlapping clustering for social network analysis"
    - "learning mixtures of ranking models"
    - "maximum likelihood estimation of functionals of discrete distributions"
    - "on the approximation 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.DM(Discrete Mathematics)