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 "balancing gaussian vectors in high dimension"):
  Abstract: Motivated by problems in controlled experiments, we study the discrepancy of random matrices with continuous entries where the number of columns $n$ is much larger than the number of rows $m$. Our first result shows that if $\omega(1) \leq m \leq o(n)$, a matrix with i.i.d. standard Gaussian entries has discrepancy $\Theta(\sqrt{n} \, 2^{-n/m})$ with high probability. This provides sharp guarantees for Gaussian discrepancy in a regime that had not been considered before in the existing literature. Our results also apply to a more general family of random matrices with continuous i.i.d. entries, assuming that $m \leq O(n/\log{n})$. The proof is non-constructive and is an application of the second moment method. Our second result is algorithmic and applies to random matrices whose entries are i.i.d. and have a Lipschitz density. We present a randomized polynomial-time algorithm that achieves discrepancy $e^{-\Omega(\log^2(n)/m)}$ with high probability, provided that $m \leq O(\sqrt{\log{n}})$. In the one-dimensional case, this matches the best known algorithmic guarantees due to Karmarkar--Karp. For higher dimensions $2 \leq m \leq O(\sqrt{\log{n}})$, this establishes the first efficient algorithm achieving discrepancy smaller than $O( \sqrt{m} )$.

Includes members:
  Member "on the discrepancy of random low degree set systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization")
  Member "reducibility and computational lower bounds for problems with planted sparse structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarially robust low dimensional representations")
  Member "near linear time approximation algorithms for optimal transport via sinkhorn iteration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geometric dataset distances via optimal transport")
  Member "deterministic discrepancy minimization via the multiplicative weight update method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online vector balancing and geometric discrepancy")
  Member "number balancing is as hard as minkowski s theorem and shortest vector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equal subset sum faster than the meet in the middle")
  Member "constructive discrepancy minimization for convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online vector balancing and geometric discrepancy")
  Member "constructive algorithms for discrepancy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalised pattern matching revisited")
  Member "analysis of the karmarkar karp differencing algorithm":
    also connected through other hyperedges:
      (none)

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.DM(Discrete Mathematics)