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: "deterministic discrepancy minimization via the multiplicative weight update method"
  Abstract: A well-known theorem of Spencer shows that any set system with $n$ sets over $n$ elements admits a coloring of discrepancy $O(\sqrt{n})$. While the original proof was non-constructive, recent progress brought polynomial time algorithms by Bansal, Lovett and Meka, and Rothvoss. All those algorithms are randomized, even though Bansal's algorithm admitted a complicated derandomization. #R##N#We propose an elegant deterministic polynomial time algorithm that is inspired by Lovett-Meka as well as the Multiplicative Weight Update method. The algorithm iteratively updates a fractional coloring while controlling the exponential weights that are assigned to the set constraints. #R##N#A conjecture by Meka suggests that Spencer's bound can be generalized to symmetric matrices. We prove that $n \times n$ matrices that are block diagonal with block size $q$ admit a coloring of discrepancy $O(\sqrt{n} \cdot \sqrt{\log(q)})$. #R##N#Bansal, Dadush and Garg recently gave a randomized algorithm to find a vector $x$ with entries in $\lbrace{-1,1\rbrace}$ with $\|Ax\|_{\infty} \leq O(\sqrt{\log n})$ in polynomial time, where $A$ is any matrix whose columns have length at most 1. We show that our method can be used to deterministically obtain such a vector.

Connected through hyperedge HE-1 (induced by source paper "online vector balancing and geometric discrepancy"):
  with members:
    - "on line balancing of random inputs"
    - "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"
    - "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 line balancing of random inputs"
    - "on the discrepancy of random low degree set systems"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "algorithmic discrepancy beyond partial coloring"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "balancing gaussian vectors in high dimension"):
  with members:
    - "on the discrepancy of random low degree set systems"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "number balancing is as hard as minkowski s theorem and shortest vector"
    - "constructive discrepancy minimization for convex sets"
    - "constructive algorithms for discrepancy minimization"
    - "analysis of the karmarkar karp differencing algorithm"

Connected through hyperedge HE-4 (induced by source paper "spherical discrepancy minimization and algorithmic lower bounds for covering the sphere"):
  with members:
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "algorithmic discrepancy beyond partial coloring"
    - "constructive discrepancy minimization by walking on the edges"
    - "spherical coverage verification"

Connected through hyperedge HE-5 (induced by source paper "algorithms to approximate column sparse packing problems"):
  with members:
    - "attenuate locally win globally an attenuation based framework for online stochastic matching with timeouts"
    - "random order contention resolution schemes"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "improved algorithmic bounds for discrepancy of sparse set systems"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "non negative submodular stochastic probing via stochastic contention resolution schemes"
    - "improved approximation algorithms for stochastic matching"

Connected through hyperedge HE-6 (induced by source paper "the gram schmidt walk a cure for the banaszczyk blues"):
  with members:
    - "algorithmic discrepancy beyond partial coloring"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "approximation friendly discrepancy rounding"
    - "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-7 (induced by source paper "algorithmic discrepancy beyond partial coloring"):
  with members:
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "approximation friendly discrepancy rounding"
    - "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-8 (induced by source paper "towards a constructive version of banaszczyk s vector balancing theorem"):
  with members:
    - "constructive discrepancy minimization for convex sets"
    - "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.DM(Discrete Mathematics)