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 "online vector balancing and geometric discrepancy"):
  Abstract: We consider an online vector balancing question where T vectors, chosen from an arbitrary distribution over [−1,1]^n, arrive one-by-one and must be immediately given a ± sign. The goal is to keep the discrepancy small as possible. A concrete example is the online interval discrepancy problem where T points are sampled uniformly in [0,1], and the goal is to immediately color them ± such that every sub-interval remains nearly balanced. As random coloring incurs Ω(T^{1/2}) discrepancy, while the offline bounds are Θ((nlogT)^{1/2}) for vector balancing and 1 for interval balancing, a natural question is whether one can (nearly) match the offline bounds in the online setting for these problems. One must utilize the stochasticity as in the worst-case scenario it is known that discrepancy is Ω(T^{1/2}) for any online algorithm.Bansal and Spencer recently show an O(n\sqrt{logT}) bound when each coordinate is independent. When there are dependencies among the coordinates, the problem becomes much more challenging, as evidenced by a recent work of Jiang, Kulkarni, and Singla that gives a non-trivial O(T^{1/loglogT}) bound for online interval discrepancy. Although this beats random coloring, it is still far from the offline bound.In this work, we introduce a new framework for online vector balancing when the input distribution has dependencies across coordinates. This lets us obtain a poly(n,logT) bound for online vector balancing under arbitrary input distributions, and a poly(logT) bound for online interval discrepancy. Our framework is powerful enough to capture other well-studied geometric discrepancy problems; e.g., a poly(logd(T)) bound for the online d-dimensional Tusnady's problem. A key new technical ingredient is an {\em anti-concentration} inequality for sums of pairwise uncorrelated random variables.

Includes members:
  Member "on line balancing of random inputs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization")
  Member "online geometric discrepancy for stochastic arrivals with applications to envy minimization":
    also connected through other hyperedges:
      (none)
  Member "the gram schmidt walk a cure for the banaszczyk blues":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spectral approach to network design")
  Member "algorithmic discrepancy beyond partial coloring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a spectral approach to network design")
  Member "deterministic discrepancy minimization via the multiplicative weight update method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization")
  Member "efficient algorithms for discrepancy minimization in convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization")
  Member "constructive discrepancy minimization for convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "balancing gaussian vectors in high dimension")
  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")

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)