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: "real stable polynomials and matroids optimization and counting"
  Abstract: A great variety of fundamental optimization and counting problems arising in computer science, mathematics and physics can be reduced to one of the following computational tasks involving polynomials and set systems: given an $m$-variate real polynomial $g$ and a family of subsets $B$ of $[m]$, (1) find $S\in B$ such that the monomial in $g$ corresponding to $S$ has the largest coefficient in $g$, or (2) compute the sum of coefficients of monomials in $g$ corresponding to all the sets in $B$. Special cases of these problems, such as computing permanents, sampling from DPPs and maximizing subdeterminants have been topics of recent interest in theoretical computer science. #R##N#In this paper we present a general convex programming framework geared to solve both of these problems. We show that roughly, when $g$ is a real stable polynomial with non-negative coefficients and $B$ is a matroid, the integrality gap of our relaxation is finite and depends only on $m$ (and not on the coefficients of g). #R##N#Prior to our work, such results were known only in sporadic cases that relied on the structure of $g$ and $B$; it was not even clear if one could formulate a convex relaxation that has a finite integrality gap beyond these special cases. Two notable examples are a result by Gurvits on the van der Waerden conjecture for real stable $g$ when $B$ is a single element and a result by Nikolov and Singh for multilinear real stable polynomials when $B$ is a partition matroid. Our work, which encapsulates most interesting cases of $g$ and $B$, benefits from both - we were inspired by the latter in deriving the right convex programming relaxation and the former in establishing the integrality gap. However, proving our results requires significant extensions of both; in that process we come up with new notions and connections between stable polynomials and matroids which should be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "dynamic sampling from graphical models"):
  with members:
    - "distributed symmetry breaking in sampling optimal distributed randomly coloring with fewer colors"
    - "a simple parallel and distributed sampling technique local glauber dynamics"
    - "tight bounds for popping algorithms"
    - "sublogarithmic distributed algorithms for lov asz local lemma and the complexity hierarchy"
    - "what can be sampled locally"
    - "a polynomial time approximation algorithm for all terminal network reliability"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"

Connected through hyperedge HE-2 (induced by source paper "composable core sets for determinant maximization problems via spectral spanners"):
  with members:
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized rounding for the largest simplex problem"
    - "linear bandits in high dimension and recommendation systems"
    - "learning determinantal point processes"
    - "exponential inapproximability of selecting a maximum volume sub matrix"

Connected through hyperedge HE-3 (induced by source paper "proportional volume sampling and approximation algorithms for a optimal design"):
  with members:
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "on largest volume simplices and sub determinants"
    - "randomized rounding for the largest simplex problem"
    - "deterministic feature selection for k means clustering"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-4 (induced by source paper "log concave polynomials i entropy and a deterministic approximation algorithm for counting bases of matroids"):
  with members:
    - "nash social welfare for indivisible items under separable piecewise linear concave utilities"
    - "entropy optimization and counting"
    - "a polynomial time algorithm to approximate the mixed volume within a simply exponential factor"

Connected through hyperedge HE-5 (induced by source paper "the ising partition function zeros and deterministic approximation"):
  with members:
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "random cluster dynamics for the ising model is rapidly mixing"
    - "uniqueness spatial mixing and approximation for ferromagnetic 2 spin systems"
    - "fptas for hardcore and ising models on hypergraphs"
    - "counting hypergraph matchings up to uniqueness threshold"
    - "the complexity of approximately counting in 2 spin systems on k uniform bounded degree hypergraphs"
    - "the kadison singer problem for strongly rayleigh measures and applications to asymmetric tsp"

Connected through hyperedge HE-6 (induced by source paper "belief propagation bethe approximation and polynomials"):
  with members:
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "the bethe permanent of a nonnegative matrix"
    - "entropy optimization and counting"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "the bethe partition function of log supermodular graphical models"
    - "partition functions of normal factor graphs"
    - "belief propagation and loop calculus for the permanent of a non negative matrix"

Connected through hyperedge HE-7 (induced by source paper "maximum entropy distributions bit complexity and stability"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "on the complexity of constrained determinantal point processes"
    - "entropy optimization and counting"

Connected through hyperedge HE-8 (induced by source paper "subdeterminant maximization via nonconvex relaxations and anti concentration"):
  with members:
    - "a generalization of permanent inequalities and applications in counting and optimization"
    - "how to be fair and diverse"
    - "on the complexity of constrained determinantal point processes"
    - "analysis of boolean functions"
    - "on largest volume simplices and sub determinants"
    - "randomized rounding for the largest simplex problem"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"

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)