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: "on largest volume simplices and sub determinants"
  Abstract: We show that the problem of finding the simplex of largest volume in the convex hull of $n$ points in $\mathbb{Q}^d$ can be approximated with a factor of $O(\log d)^{d/2}$ in polynomial time. This improves upon the previously best known approximation guarantee of $d^{(d-1)/2}$ by Khachiyan. On the other hand, we show that there exists a constant $c>1$ such that this problem cannot be approximated with a factor of $c^d$, unless $P=NP$. % This improves over the $1.09$ inapproximability that was previously known. Our hardness result holds even if $n = O(d)$, in which case there exists a $\bar c\,^{d}$-approximation algorithm that relies on recent sampling techniques, where $\bar c$ is again a constant. We show that similar results hold for the problem of finding the largest absolute value of a subdeterminant of a $d\times n$ matrix.

Connected through hyperedge HE-1 (induced by source paper "maximizing the number of spanning trees in a connected graph"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"

Connected through hyperedge HE-2 (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"
    - "real stable polynomials and matroids optimization and counting"
    - "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-3 (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"
    - "real stable polynomials and matroids optimization and counting"
    - "analysis of boolean functions"
    - "randomized rounding for the largest simplex problem"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"

Connected through hyperedge HE-4 (induced by source paper "real stable polynomials and matroids optimization and counting"):
  with members:
    - "combinatorial determinantal point processes"
    - "how to be fair and diverse"
    - "nash social welfare matrix permanent and stable polynomials"
    - "randomized rounding for the largest simplex problem"
    - "entropy optimization and counting"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-5 (induced by source paper "on sampling and greedy map inference of constrained determinantal point processes"):
  with members:
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-6 (induced by source paper "randomized rounding for the largest simplex problem"):
  with members:
    - "the geometry of differential privacy the sparse and approximate cases"
    - "optimal column based low rank matrix reconstruction"
    - "exponential inapproximability of selecting a maximum volume sub matrix"
    - "efficient volume sampling for row column subset selection"

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.CG(Computational Geometry)