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 "efficient algorithms for discrepancy minimization in convex sets"):
  Abstract: A result of Spencer states that every collection of $n$ sets over a universe of size $n$ has a coloring of the ground set with $\{-1,+1\}$ of discrepancy $O(\sqrt{n})$. A geometric generalization of this result was given by Gluskin (see also Giannopoulos) who showed that every symmetric convex body $K\subseteq R^n$ with Gaussian measure at least $e^{-\epsilon n}$, for a small $\epsilon>0$, contains a point $y\in K$ where a constant fraction of coordinates of $y$ are in $\{-1,1\}$. This is often called a partial coloring result. While both these results were inherently non-algorithmic, recently Bansal (see also Lovett-Meka) gave a polynomial time algorithm for Spencer's setting and Rothvo\ss gave a randomized polynomial time algorithm obtaining the same guarantee as the result of Gluskin and Giannopoulos. #R##N#This paper has several related results. First we prove another constructive version of the result of Gluskin and Giannopoulos via an optimization of a linear function. This implies a linear programming based algorithm for combinatorial discrepancy obtaining the same result as Spencer. Our second result gives a new approach to obtains partial colorings and shows that every convex body $K\subseteq R^n$, possibly non-symmetric, with Gaussian measure at least $e^{-\epsilon n}$, for a small $\epsilon>0$, contains a point $y\in K$ where a constant fraction of coordinates of $y$ are in $\{-1,1\}$. Finally, we give a simple proof that shows that for any $\delta >0$ there exists a constant $c>0$ such that given a body $K$ with $\gamma_n(K)\geq \delta$, a uniformly random $x$ from $\{-1,1\}^n$ is in $cK$ with constant probability. This gives an algorithmic version of a special case of the result of Banaszczyk.

Includes members:
  Member "constructive discrepancy minimization for convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "an optimal algorithm for 1 d cutting stock problem")
  Member "a size sensitive discrepancy bound for set systems of bounded primal shatter dimension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "epsilon approximations and epsilon nets")
  Member "the geometry of differential privacy the sparse and approximate cases":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "generalised pattern matching revisited")
  Member "constructive algorithms for discrepancy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)