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 4 approximation for scheduling on a single machine with general cost function"
  Abstract: We consider a single machine scheduling problem that seeks to minimize a generalized cost function: given a subset of jobs we must order them so as to minimize $\sum f_j(C_j)$, where $C_j$ is the completion time of job $j$ and $f_j$ is a job-dependent cost function. This problem has received a considerably amount of attention lately, partly because it generalizes a large number of sequencing problems while still allowing constant approximation guarantees. #R##N#In a recent paper, Cheung and Shmoys provided a primal-dual algorithm for the problem and claimed that is a 2-approximation. In this paper we show that their analysis cannot yield an approximation guarantee better than $4$. We then cast their algorithm as a local ratio algorithm and show that in fact it has an approximation ratio of $4$. Additionally, we consider a more general problem where jobs has release dates and can be preempted. For this version we give a $4\kappa$-approximation algorithm where $\kappa$ is the number of distinct release dates.

Connected through hyperedge HE-1 (induced by source paper "scheduling to approximate minimization objectives on identical machines"):
  with members:
    - "a primal dual approximation algorithm for min sum single machine scheduling problems"
    - "the geometry of scheduling"

Connected through hyperedge HE-2 (induced by source paper "small extended formulation for knapsack cover inequalities from monotone circuits"):
  with members:
    - "extension complexity of independent set polytopes"
    - "a primal dual approximation algorithm for min sum single machine scheduling problems"
    - "addition is exponentially harder than counting for shallow monotone circuits"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "the matching polytope has exponential extension complexity"
    - "approximation limits of linear programs beyond hierarchies"
    - "the geometry of scheduling"

Connected through hyperedge HE-3 (induced by source paper "a lasserre lower bound for the min sum single machine scheduling problem"):
  with members:
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"
    - "sum of squares lower bounds from pairwise independence"
    - "sum of squares lower bounds for planted clique"
    - "approximability and proof complexity"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"
    - "integrality gaps of linear and semi definite programming relaxations for knapsack"
    - "the geometry of scheduling"

Connected through hyperedge HE-4 (induced by source paper "sum of squares hierarchy lower bounds for symmetric formulations"):
  with members:
    - "tight sum of squares lower bounds for binary polynomial optimization problems"
    - "on the sum of squares degree of symmetric quadratic functions"
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"
    - "a lasserre lower bound for the min sum single machine scheduling problem"
    - "sum of squares lower bounds from pairwise independence"
    - "sum of squares lower bounds for planted clique"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-5 (induced by source paper "how unsplittable flow covering helps scheduling with job dependent cost functions"):
  with members:
    - "a primal dual approximation algorithm for min sum single machine scheduling problems"
    - "dual techniques for scheduling on a machine with varying speed"
    - "a constant factor approximation algorithm for unsplittable flow on paths"
    - "the geometry of scheduling"

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)