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: "subdeterminant maximization via nonconvex relaxations and anti concentration"
  Abstract: Several fundamental problems that arise in optimization and computer science can be cast as follows: Given vectors $v_1,\ldots,v_m \in \mathbb{R}^d$ and a constraint family ${\cal B}\subseteq 2^{[m]}$, find a set $S \in \cal{B}$ that maximizes the squared volume of the simplex spanned by the vectors in $S$. A motivating example is the data-summarization problem in machine learning where one is given a collection of vectors that represent data such as documents or images. The volume of a set of vectors is used as a measure of their diversity, and partition or matroid constraints over $[m]$ are imposed in order to ensure resource or fairness constraints. Recently, Nikolov and Singh presented a convex program and showed how it can be used to estimate the value of the most diverse set when ${\cal B}$ corresponds to a partition matroid. This result was recently extended to regular matroids in works of Straszak and Vishnoi, and Anari and Oveis Gharan. The question of whether these estimation algorithms can be converted into the more useful approximation algorithms -- that also output a set -- remained open. #R##N#The main contribution of this paper is to give the first approximation algorithms for both partition and regular matroids. We present novel formulations for the subdeterminant maximization problem for these matroids; this reduces them to the problem of finding a point that maximizes the absolute value of a nonconvex function over a Cartesian product of probability simplices. The technical core of our results is a new anti-concentration inequality for dependent random variables that allows us to relate the optimal value of these nonconvex functions to their value at a random point. Unlike prior work on the constrained subdeterminant maximization problem, our proofs do not rely on real-stability or convexity and could be of independent interest both in algorithms and complexity.

Connected through hyperedge HE-1 (induced by source paper "composable core sets for determinant maximization a simple near optimal algorithm"):
  with members:
    - "composable core sets for determinant maximization problems via spectral spanners"
    - "streaming non monotone submodular maximization personalized video summarization on the fly"
    - "how to be fair and diverse"
    - "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 composable core sets for distributed submodular maximization"
    - "streaming algorithms for submodular function maximization"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-2 (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"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"
    - "sampling random spanning trees faster than matrix multiplication"

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)