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: "proportional volume sampling and approximation algorithms for a optimal design"
  Abstract: We study the optimal design problems where the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector in $d$ dimensions. We study the $A$-optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of the vector being measured. The problem also finds applications in sensor placement in wireless networks, sparse least squares regression, feature selection for $k$-means clustering, and matrix approximation. In this paper, we introduce proportional volume sampling to obtain improved approximation algorithms for $A$-optimal design. Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm. Our results nearly optimal bounds in the asymptotic regime when the number of measurements done, $k$, is significantly more than the dimension $d$. We also give first approximation algorithms when $k$ is small including when $k=d$. The proportional volume-sampling algorithm also gives approximation algorithms for other optimal design objectives such as $D$-optimal design and generalized ratio objective matching or improving previous best known results. Interestingly, we show that a similar guarantee cannot be obtained for the $E$-optimal design problem. We also show that the $A$-optimal design problem is NP-hard to approximate within a fixed constant when $k=d$.

Connected through hyperedge HE-1 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"
    - "algorithmic discrepancy beyond partial coloring"

Connected through hyperedge HE-2 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "guarantees for greedy maximization of non submodular functions with applications"

Connected through hyperedge HE-3 (induced by source paper "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"):
  with members:
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "efficient sampling for k determinantal point processes"

Connected through hyperedge HE-4 (induced by source paper "network design for s t effective resistance"):
  with members:
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "parameters of two prover one round game and the hardness of connectivity problems"
    - "iterative rounding approximation algorithms for degree bounded node connectivity network design"
    - "approximating minimum cost k node connected subgraphs via independence free graphs"
    - "reductions between expansion problems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-5 (induced by source paper "iterative projection and matching finding structure preserving representatives and its application to computer vision"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "deep bayesian active learning with image data"
    - "subsampling for ridge regression via regularized volume sampling"
    - "robust and scalable column row sampling from corrupted big data"
    - "video summarization with long short term memory"

Connected through hyperedge HE-6 (induced by source paper "composable core sets for determinant maximization problems via spectral spanners"):
  with members:
    - "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"
    - "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"

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)