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: "randomized rounding for the largest simplex problem"
  Abstract: The maximum volume $j$-simplex problem asks to compute the $j$-dimensional simplex of maximum volume inside the convex hull of a given set of $n$ points in $\mathbb{Q}^d$. We give a deterministic approximation algorithm for this problem which achieves an approximation ratio of $e^{j/2 + o(j)}$. The problem is known to be $\mathrm{NP}$-hard to approximate within a factor of $c^{j}$ for some constant $c > 1$. Our algorithm also gives a factor $e^{j + o(j)}$ approximation for the problem of finding the principal $j\times j$ submatrix of a rank $d$ positive semidefinite matrix with the largest determinant. We achieve our approximation by rounding solutions to a generalization of the $D$-optimal design problem, or, equivalently, the dual of an appropriate smallest enclosing ellipsoid problem. Our arguments give a short and simple proof of a restricted invertibility principle for determinants.

Connected through hyperedge HE-1 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "batch active preference based learning of reward functions"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-2 (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"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "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"

Connected through hyperedge HE-3 (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-4 (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"
    - "real stable polynomials and matroids optimization and counting"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "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-5 (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"
    - "on largest volume simplices and sub determinants"
    - "deterministic feature selection for k means clustering"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-6 (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"
    - "on largest volume simplices and sub determinants"
    - "a robust khintchine inequality and algorithms for computing optimal constants in fourier analysis and high dimensional geometry"

Connected through hyperedge HE-7 (induced by source paper "combinatorial determinantal point processes"):
  with members:
    - "approximating the largest root and applications to interlacing families"
    - "how to be fair and diverse"
    - "submodular meets structured finding diverse subsets in exponentially large structured item sets"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "entropy optimization and counting"
    - "solving the apparent diversity accuracy dilemma of recommender systems"
    - "efficient volume sampling for row column subset selection"
    - "a polynomial time approximation algorithm for the number of k matchings in bipartite graphs"

Connected through hyperedge HE-8 (induced by source paper "batched gaussian process bandit optimization via determinantal point processes"):
  with members:
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "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)