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 "on modeling local search with special purpose combinatorial optimization hardware"):
  Abstract: Many combinatorial scientific computing problems are NP-hard which in practice requires using heuristics that either decompose a large-scale problem and solve many smaller local subproblems in parallel or iteratively improve the solution by local processing for sufficiently many steps to achieve a good quality/performance trade-off. These subproblems are NP-hard by themselves and the quality of their solution that depends on the subproblem size vitally affects the global solution of the entire problem. As we approach the physical limits predicted by Moore's law, a variety of specialized combinatorial optimization hardware (such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators) is emerging to tackle specialized tasks in different domains. Many researchers anticipate that in the near future, these devices will be hybridized with high-performance computing systems to tackle large-scale problems which will open a door for the next breakthrough in performance of the combinatorial scientific computing algorithms. Most of these devices solve the Ising combinatorial optimization model that can represent many fundamental combinatorial scientific computing models. In this paper, we tackle the main challenges of problem size and precision limitation that the Ising hardware model typically suffers from. Although, our demonstration uses one of these novel devices, the models we propose can be generally used in any local search and refinement solvers that are broadly employed in combinatorial scientific computing algorithms.

Includes members:
  Member "detecting multiple communities using quantum annealing on the d wave system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ising based consensus clustering on specialized hardware")
  Member "a tutorial on formulating and using qubo models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph clustering via qubo and digital annealing")
  Member "nonnegative binary matrix factorization with a d wave quantum annealer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support vector machines on the d wave quantum annealer")
  Member "relaxation based coarsening for multilevel hypergraph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph partitioning with embeddings")
  Member "free energy based reinforcement learning using a quantum processor":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smooth structured prediction using quantum and classical gibbs samplers")
  Member "a novel graph based approach for determining molecular similarity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a quantum annealing based approach to extreme clustering")
  Member "recent advances in graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "advanced coarsening schemes for graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)