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: "tight lower bounds for the workflow satisfiability problem based on the strong exponential time hypothesis"
  Abstract: The Workflow Satisfiability Problem (WSP) asks whether there exists an assignment of authorized users to the steps in a workflow specification, subject to certain constraints on the assignment. The problem is NP-hard even when restricted to just not equals constraints. Since the number of steps $k$ is relatively small in practice, Wang and Li (2010) introduced a parametrisation of WSP by $k$. Wang and Li (2010) showed that, in general, the WSP is W[1]-hard, i.e., it is unlikely that there exists a fixed-parameter tractable (FPT) algorithm for solving the WSP. Crampton et al. (2013) and Cohen et al. (2014) designed FPT algorithms of running time $O^*(2^{k})$ and $O^*(2^{k\log_2 k})$ for the WSP with so-called regular and user-independent constraints, respectively. In this note, we show that there are no algorithms of running time $O^*(2^{ck})$ and $O^*(2^{ck\log_2 k})$ for the two restrictions of WSP, respectively, with any $c<1$, unless the Strong Exponential Time Hypothesis fails.

Connected through hyperedge HE-1 (induced by source paper "pattern based approach to the workflow satisfiability problem with user independent constraints"):
  with members:
    - "algorithms for the workflow satisfiability problem engineered for counting constraints"
    - "the bi objective workflow satisfiability problem and workflow resiliency"
    - "valued workflow satisfiability problem"
    - "polynomial kernels and user reductions for the workflow satisfiability problem"
    - "iterative plan construction for the workflow satisfiability problem"
    - "parameterized complexity results for exact bayesian network structure learning"
    - "on the parameterized complexity and kernelization of the workflow satisfiability problem"
    - "empirical evaluation of construction heuristics for the multidimensional assignment problem"

Connected through hyperedge HE-2 (induced by source paper "the bi objective workflow satisfiability problem and workflow resiliency"):
  with members:
    - "valued workflow satisfiability problem"
    - "pattern backtracking algorithm for the workflow satisfiability problem"
    - "iterative plan construction for the workflow satisfiability problem"
    - "constraint expressions and workflow satisfiability"
    - "on the parameterized complexity and kernelization of the workflow satisfiability problem"
    - "fixed parameter tractability of workflow satisfiability in the presence of seniority constraints"

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)