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: "pipage rounding pessimistic estimators and matrix concentration"
  Abstract: Pipage rounding is a dependent random sampling technique that has several interesting properties and diverse applications. One property that has been particularly useful is negative correlation of the resulting vector. Unfortunately negative correlation has its limitations, and there are some further desirable properties that do not seem to follow from existing techniques. In particular, recent concentration results for sums of independent random matrices are not known to extend to a negatively dependent setting. #R##N#We introduce a simple but useful technique called concavity of pessimistic estimators. This technique allows us to show concentration of submodular functions and concentration of matrix sums under pipage rounding. The former result answers a question of Chekuri et al. (2009). To prove the latter result, we derive a new variant of Lieb's celebrated concavity theorem in matrix analysis. #R##N#We provide numerous applications of these results. One is to spectrally-thin trees, a spectral analog of the thin trees that played a crucial role in the recent breakthrough on the asymmetric traveling salesman problem. We show a polynomial time algorithm that, given a graph where every edge has effective conductance at least $\kappa$, returns an $O(\kappa^{-1} \cdot \log n / \log \log n)$-spectrally-thin tree. There are further applications to rounding of semidefinite programs, to the column subset selection problem, and to a geometric question of extracting a nearly-orthonormal basis from an isotropic distribution.

Connected through hyperedge HE-1 (induced by source paper "tail bounds for volume sampled linear regression"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "how to be fair and diverse"
    - "sketching as a tool for numerical linear algebra"
    - "randomized algorithms for matrices and data"
    - "note on sampling without replacing from a finite collection of matrices"

Connected through hyperedge HE-2 (induced by source paper "on a generalization of iterated and randomized rounding"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "algorithmic discrepancy beyond partial coloring"
    - "approximation friendly discrepancy rounding"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "entropy optimization and counting"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-3 (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"
    - "randomized rounding for the largest simplex problem"
    - "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-4 (induced by source paper "effective resistance reducing flows spectrally thin trees and asymmetric tsp"):
  with members:
    - "approximating atsp by relaxing connectivity"
    - "the kadison singer problem for strongly rayleigh measures and applications to asymmetric tsp"
    - "a near optimal approximation algorithm for asymmetric tsp on embedded graphs"
    - "a randomized rounding algorithm for the asymmetric traveling salesman problem"
    - "the asymmetric traveling salesman problem on graphs with bounded genus"
    - "twice ramanujan sparsifiers"
    - "graph sparsification by effective resistances"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-5 (induced by source paper "an improved approximation for k median and positive correlation in budgeted optimization"):
  with members:
    - "tight algorithms for vertex cover with hard capacities on multigraphs and hypergraphs"
    - "approximating k median via pseudo approximation"
    - "a systematic approach to bound factor revealing lps and its application to the metric and squared metric facility location problems"
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "greedy facility location algorithms analyzed using dual fitting with factor revealing lp"

Connected through hyperedge HE-6 (induced by source paper "the kadison singer problem for strongly rayleigh measures and applications to asymmetric tsp"):
  with members:
    - "entropy optimization and counting"
    - "twice ramanujan sparsifiers"

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)