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: "openmp loop scheduling revisited making a case for more schedules"
  Abstract: In light of continued advances in loop scheduling, this work revisits the OpenMP loop scheduling by outlining the current state of the art in loop scheduling and presenting evidence that the existing OpenMP schedules are insufficient for all combinations of applications, systems, and their characteristics. A review of the state of the art shows that due to the specifics of the parallel applications, the variety of computing platforms, and the numerous performance degradation factors, no single loop scheduling technique can be a 'one-fits-all' solution to effectively optimize the performance of all parallel applications in all situations. The impact of irregularity in computational workloads and hardware systems, including operating system noise, on the performance of parallel applications, results in performance loss and has often been neglected in loop scheduling research, in particular, the context of OpenMP schedules. Existing dynamic loop self-scheduling techniques, such as trapezoid self-scheduling, factoring, and weighted factoring, offer an unexplored potential to alleviate this degradation in OpenMP due to the fact that they explicitly target the minimization of load imbalance and scheduling overhead. Through theoretical and experimental evaluation, this work shows that these loop self-scheduling methods provide a benefit in the context of OpenMP. In conclusion, OpenMP must include more schedules to offer a broader performance coverage of applications executing on an increasing variety of heterogeneous shared memory computing platforms.

Connected through hyperedge HE-1 (induced by source paper "an approach for realistically simulating the performance of scientific applications on high performance computing systems"):
  with members:
    - "sil an approach for adjusting applications to heterogeneous systems under perturbations"
    - "experimental verification and analysis of dynamic loop scheduling in scientific applications"
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "efficient generation of parallel spin images using dynamic loop scheduling"
    - "exploring the relation between two levels of scheduling using a novel simulation approach"

Connected through hyperedge HE-2 (induced by source paper "online research report rdlb a novel approach for robust dynamic load balancing of scientific applications with parallel independent tasks"):
  with members:
    - "sil an approach for adjusting applications to heterogeneous systems under perturbations"
    - "dynamic loop scheduling using mpi passive target remote memory access"
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "efficient generation of parallel spin images using dynamic loop scheduling"

Connected through hyperedge HE-3 (induced by source paper "rdlb a novel approach for robust dynamic load balancing of scientific applications with parallel independent tasks"):
  with members:
    - "sil an approach for adjusting applications to heterogeneous systems under perturbations"
    - "dynamic loop scheduling using mpi passive target remote memory access"
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "efficient generation of parallel spin images using dynamic loop scheduling"

Connected through hyperedge HE-4 (induced by source paper "hierarchical dynamic loop self scheduling on distributed memory systems using an mpi mpi approach"):
  with members:
    - "dynamic loop scheduling using mpi passive target remote memory access"
    - "efficient generation of parallel spin images using dynamic loop scheduling"

Connected through hyperedge HE-5 (induced by source paper "two level dynamic load balancing for high performance scientific applications"):
  with members:
    - "efficient generation of parallel spin images using dynamic loop scheduling"
    - "exploring the relation between two levels of scheduling using a novel simulation approach"

Connected through hyperedge HE-6 (induced by source paper "toward a standard interface for user defined scheduling in openmp"):
  with members:
    - "hybrid static dynamic scheduling for already optimized dense matrix factorization"

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.DC(Distributed, Parallel, and Cluster Computing)