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: "on line balancing of random inputs"
  Abstract: We consider an online vector balancing game where vectors vt, chosen uniformly at random in {−1,+1}n, arrive over time and a sign xt∈{−1,+1} must be picked immediately upon the arrival of vt. The goal is to minimize the L∞ norm of the signed sum ∑txtvt. We give an online strategy for picking the signs xt that has value O(n1/2) with high probability. Up to constants, this is the best possible even when the vectors are given in advance.

Connected through hyperedge HE-1 (induced by source paper "online vector balancing and geometric discrepancy"):
  with members:
    - "online geometric discrepancy for stochastic arrivals with applications to envy minimization"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "constructive discrepancy minimization for convex sets"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-2 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization"):
  with members:
    - "on the discrepancy of random low degree set systems"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "algorithmic discrepancy beyond partial coloring"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "fair assignment of indivisible objects under ordinal preferences"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

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)