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 "a new approximation technique for resource allocation problems"):
  Abstract: We develop a rounding method based on random walks in polytopes, which leads to improved approximation algorithms and integrality gaps for several assignment problems that arise in resource allocation and scheduling. In particular, it generalizes the work of Shmoys and Tardos on the generalized assignment problem to the setting where some jobs can be dropped. New concentration bounds for random bipartite matching are developed as well.

Includes members:
  Member "matroidal degree bounded minimum spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "degree bounded generalized polymatroids and approximating the metric many visits tsp")
  Member "on generalizations of network design problems with degree bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spectral approach to network design")
  Member "santa claus schedules jobs on unrelated machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weighted completion time minimization for unrelated machines via iterative fair contention resolution")
  Member "approximating scheduling machines with capacity constraints":
    also connected through other hyperedges:
      (none)
  Member "on allocating goods to maximize fairness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weighted completion time minimization for unrelated machines via iterative fair contention resolution")
  Member "scheduling with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the partition set cover problem")
  Member "adaptive uncertainty resolution in bayesian combinatorial optimization problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the markovian price of information")

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)