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 "optimization and reoptimization in scheduling problems"):
  Abstract: Parallel machine scheduling has been extensively studied in the past decades, with applications ranging from production planning to job processing in large computing clusters. In this work we study some of these fundamental optimization problems, as well as their parameterized and reoptimization variants. #R##N#We first present improved bounds for job scheduling on unrelated parallel machines, with the objective of minimizing the latest completion time (makespan) of the schedule. We consider the subclass of fully-feasible instances, in which the processing time of each job, on any machine, does not exceed the minimum makespan. The problem is known to be hard to approximate within factor 4/3 already in this subclass. Although fully-feasible instances are hard to identify, we give a polynomial time algorithm that yields for such instances a schedule whose makespan is better than twice the optimal, the best known ratio for general instances. Moreover, we show that our result is robust under small violations of feasibility constraints. #R##N#We further study the power of parameterization. We show that makespan minimization on unrelated machines admits a parameterized approximation scheme, where the parameter used is the number of processing times that are large relative to the latest completion time of the schedule. We also present an FPT algorithm for the graph-balancing problem, which corresponds to the instances of the restricted assignment problem where each job can be processed on at most 2 machines. #R##N#Finally, motivated by practical scenarios, we initiate the study of reoptimization in job scheduling on identical and uniform machines, with the objective of minimizing the makespan. We develop reapproximation algorithms that yield in both models the best possible approximation ratio of $(1+\epsilon)$, for any $\epsilon >0$, with respect to the minimum makespan.

Includes members:
  Member "reallocation problems in scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reoptimization via gradual transformations")
  Member "scheduling meets fixed parameter tractability":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the parameterized tractability of the just in time flow shop scheduling problem")
  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 "on the configuration lp for scheduling on unrelated machines":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized orientable deletion")

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)