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: "a quasi polynomial approximation for the restricted assignment problem"
  Abstract: Scheduling jobs on unrelated machines and minimizing the makespan is a classical problem in combinatorial optimization. In this problem a job $j$ has a processing time $p_{ij}$ for every machine $i$. The best polynomial algorithm known goes back to Lenstra et al. and has an approximation ratio of $2$. In this paper we study the Restricted Assignment problem, which is the special case where $p_{ij}\in\{p_j,\infty\}$. We present an algorithm for this problem with an approximation ratio of $11/6 + \epsilon$ and quasi-polynomial running time $n^{\mathcal O(1/\epsilon \log(n))}$ for every $\epsilon > 0$. This closes the gap to the best estimation algorithm known for the problem with regard to quasi-polynomial running time.

Connected through hyperedge HE-1 (induced by source paper "inapproximability results for scheduling with interval and resource restrictions"):
  with members:
    - "local search breaks 1 75 for graph balancing"
    - "a tale of santa claus hypergraphs and matroids"
    - "restricted max min fair allocation"
    - "structural parameters for scheduling with assignment restrictions"
    - "ptas for ordered instances of resource allocation problems with restrictions on inclusions"
    - "graph balancing with two edge types"
    - "a combinatorial approximation algorithm for graph balancing with light hyper edges"
    - "combinatorial algorithm for restricted max min fair allocation"

Connected through hyperedge HE-2 (induced by source paper "on the extension complexity of scheduling"):
  with members:
    - "the matching polytope has exponential extension complexity"

Connected through hyperedge HE-3 (induced by source paper "local search breaks 1 75 for graph balancing"):
  with members:
    - "lazy local search meets machine scheduling"
    - "a combinatorial approximation algorithm for graph balancing with light hyper edges"
    - "quasi polynomial local search for restricted max min fair allocation"
    - "santa claus schedules jobs on unrelated machines"
    - "on allocating goods to maximize fairness"

Connected through hyperedge HE-4 (induced by source paper "scheduling on un related machines with setup times"):
  with members:
    - "empowering the configuration ip new ptas results for scheduling with setups times"
    - "symmetry exploitation for online machine covering with bounded migration"
    - "non preemptive scheduling on machines with setup times"
    - "santa claus schedules jobs on unrelated machines"

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)