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: "approximation friendly discrepancy rounding"
  Abstract: Rounding linear programs using techniques from discrepancy is a recent approach that has been very successful in certain settings. However this method also has some limitations when compared to approaches such as randomized and iterative rounding. We provide an extension of the discrepancy-based rounding algorithm due to Lovett-Meka that (i) combines the advantages of both randomized and iterated rounding, (ii) makes it applicable to settings with more general combinatorial structure such as matroids. As applications of this approach, we obtain new results for various classical problems such as linear system rounding, degree-bounded matroid basis and low congestion routing.

Connected through hyperedge HE-1 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints"):
  with members:
    - "on a generalization of iterated and randomized rounding"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "matroid and knapsack center problems"
    - "approximating k median via pseudo approximation"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

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"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "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 "the gram schmidt walk a cure for the banaszczyk blues"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "constructive discrepancy minimization for convex sets"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-4 (induced by source paper "algorithmic discrepancy beyond partial coloring"):
  with members:
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "constructive discrepancy minimization for convex sets"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-5 (induced by source paper "approximating min cost chain constrained spanning trees a reduction from weighted to unweighted problems"):
  with members:
    - "matroidal degree bounded minimum spanning trees"
    - "on generalizations of network design problems with degree bounds"

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)