human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "throughput maximization in the speed scaling setting"
  Abstract: We are given a set of $n$ jobs and a single processor that can vary its speed dynamically. Each job $J_j$ is characterized by its processing requirement (work) $p_j$, its release date $r_j$ and its deadline $d_j$. We are also given a budget of energy $E$ and we study the scheduling problem of maximizing the throughput (i.e. the number of jobs which are completed on time). We propose a dynamic programming algorithm that solves the preemptive case of the problem, i.e. when the execution of the jobs may be interrupted and resumed later, in pseudo-polynomial time. Our algorithm can be adapted for solving the weighted version of the problem where every job is associated with a weight $w_j$ and the objective is the maximization of the sum of the weights of the jobs that are completed on time. Moreover, we provide a strongly polynomial time algorithm to solve the non-preemptive unweighed case when the jobs have the same processing requirements. For the weighted case, our algorithm can be adapted for solving the non-preemptive version of the problem in pseudo-polynomial time.

Connected through hyperedge HE-1 (induced by source paper "green data centers a survey perspectives and future directions"):
  with members:
    - "new online algorithm for dynamic speed scaling with sleep state"
    - "a fully polynomial time approximation scheme for speed scaling with sleep state"
    - "sleepscale runtime joint speed scaling and sleep states management for power efficient data centers"
    - "throughput maximization in multiprocessor speed scaling"
    - "energy efficient scheduling with time and processors eligibility restrictions"
    - "speed scaling on parallel processors with migration"
    - "scalably scheduling power heterogeneous processors"
    - "polynomial time algorithms for minimum energy scheduling"

Connected through hyperedge HE-2 (induced by source paper "speed scaling with no preemptions"):
  with members:
    - "energy efficient scheduling and routing via randomized rounding"
    - "speed scaling on parallel processors with migration"
    - "power aware scheduling for makespan and flow"

Connected through hyperedge HE-3 (induced by source paper "throughput maximization in multiprocessor speed scaling"):
  with members:
    - "energy efficient scheduling and routing via randomized rounding"
    - "profitable scheduling on multiple speed scalable processors"
    - "speed scaling on parallel processors with migration"

Question: Please tell me which class the center node 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)