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 "tight approximation for variants of directed steiner tree via state tree decomposition and linear programming rounding"):
  Abstract: Directed Steiner Tree (DST) is a central problem in combinatorial optimization and theoretical computer science. Recently, Grandoni, Laekhanukit and Li and independently Ghuge and Nagarajan gave quasi-polynomial time $O(\log^2k/\log \log k)$-approximation algorithms for the problem, which is tight under popular complexity assumptions. #R##N#In this paper, we show a general framework that achieves $O(\log n \log k)$-approximation for many variants of DST. We show that if the problem has the property that the validity of the tree and its cost can be checked and computed using a bottom-to-top procedure, then the general framework can be used to produce a small-cost {multi-tree}: a tree that may contain multiple copies of a vertex or an edge. #R##N#Using the framework, we show that two prominent variants of DST, namely Length-Bounded DST (LB-DST) and Buy-at-Bulk DST with Concave Cost Functions (BaB-DST-Concave), admit $O(\log n \log k)$-approximation algorithms. In the Length-Bounded Directed Steiner Tree (LB-DST) problem, there are bounds on lengths of paths from the root to vertices in the output tree. In the Buy-at-Bulk DST problem with Concave Functions (BaB-DST-Concave), each terminal needs to receive a flow from the root and the cost of each edge is a concave function on the total amount of flow that it carries. Our results almost match the best known $O(\log ^2k/\log \log k)$ algorithm that have recently been discovered by Ghuge and Nagarajan. #R##N#Another variant that fits into the framework is the Degree-Bounded DST (DB-DST) problem. In this problem, we are additionally given a degree bound $d_v$ on each vertex $v \in V$, which imposes the constraint that $v$ is allowed to have at most $d_v$ children in the output tree. In this case, our framework gives an $O(\log^3n \log k, \log n\log k)$-bicritiera approximation, which is the first non-trivial result for the problem.

Includes members:
  Member "a quasi polynomial algorithm for submodular tree orienteering in directed graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polylogarithmic approximation algorithm for k connected directed steiner tree on quasi bipartite graphs")
  Member "online weighted degree bounded steiner networks via novel online mixed packing covering":
    also connected through other hyperedges:
      (none)
  Member "online buy at bulk network design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online weighted degree bounded steiner networks via novel online mixed packing covering")
  Member "approximating directed steiner problems via tree embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polylogarithmic approximation algorithm for k connected directed steiner tree on quasi bipartite graphs")
  Member "directed steiner tree and the lasserre hierarchy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polylogarithmic approximation algorithm for k connected directed steiner tree on quasi bipartite graphs")
  Member "bicriteria network design problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online degree bounded steiner network design")

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)