human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "algorithmic discrepancy beyond partial coloring"):
  Abstract: The partial coloring method is one of the most powerful and widely used method in combinatorial discrepancy problems. However, in many cases it leads to sub-optimal bounds as the partial coloring step must be iterated a logarithmic number of times, and the errors can add up in an adversarial way. We give a new and general algorithmic framework that overcomes the limitations of the partial coloring method and can be applied in a black-box manner to various problems. Using this framework, we give new improved bounds and algorithms for several classic problems in discrepancy. In particular, for Tusnady's problem, we give an improved $O(\log^2 n)$ bound for discrepancy of axis-parallel rectangles and more generally an $O_d(\log^dn)$ bound for $d$-dimensional boxes in $\mathbb{R}^d$. Previously, even non-constructively, the best bounds were $O(\log^{2.5} n)$ and $O_d(\log^{d+0.5}n)$ respectively. Similarly, for the Steinitz problem we give the first algorithm that matches the best known non-constructive bounds due to Banaszczyk \cite{Bana12} in the $\ell_\infty$ case, and improves the previous algorithmic bounds substantially in the $\ell_2$ case. Our framework is based upon a substantial generalization of the techniques developed recently in the context of the Koml\'{o}s discrepancy problem [BDG16].

Includes members:
  Member "deterministic discrepancy minimization via the multiplicative weight update method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online vector balancing and geometric discrepancy")
  Member "towards a constructive version of banaszczyk s vector balancing theorem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the gram schmidt walk a cure for the banaszczyk blues")
  Member "approximation friendly discrepancy rounding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints")
  Member "constructive discrepancy minimization for convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online vector balancing and geometric discrepancy")
  Member "approximating bin packing within o log opt log log opt bins":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an optimal algorithm for 1 d cutting stock problem")
  Member "the geometry of differential privacy the sparse and approximate cases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pure differentially private summation from anonymous messages")
  Member "constructive discrepancy minimization by walking on the edges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalised pattern matching revisited")
  Member "constructive algorithms for discrepancy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalised pattern matching revisited")

Question: Please tell me which class the source paper 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)