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 1 epsilon approximation for makespan scheduling with precedence constraints using lp hierarchies"
  Abstract: In a classical problem in scheduling, one has $n$ unit size jobs with a precedence order and the goal is to find a schedule of those jobs on $m$ identical machines as to minimize the makespan. It is one of the remaining four open problems from the book of Garey & Johnson whether or not this problem is $\mathbf{NP}$-hard for $m=3$. #R##N#We prove that for any fixed $\varepsilon$ and $m$, an LP-hierarchy lift of the time-indexed LP with a slightly super poly-logarithmic number of $r = (\log(n))^{\Theta(\log \log n)}$ rounds provides a $(1 + \varepsilon)$-approximation. For example Sherali-Adams suffices as hierarchy. This implies an algorithm that yields a $(1+\varepsilon)$-approximation in time $n^{O(r)}$. The previously best approximation algorithms guarantee a $2 - \frac{7}{3m+1}$-approximation in polynomial time for $m \geq 4$ and $\frac{4}{3}$ for $m=3$. Our algorithm is based on a recursive scheduling approach where in each step we reduce the correlation in form of long chains. Our method adds to the rather short list of examples where hierarchies are actually useful to obtain better approximation algorithms.

Connected through hyperedge HE-1 (induced by source paper "new dependencies of hierarchies in polynomial optimization"):
  with members:
    - "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"
    - "on the bit complexity of sum of squares proofs"
    - "exact tensor completion with sum of squares"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "the power of sherali adams relaxations for general valued csps"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-2 (induced by source paper "breaking symmetries to rescue sum of squares the case of makespan scheduling"):
  with members:
    - "proximity results and faster algorithms for integer programming using the steinitz lemma"
    - "the power of sum of squares for detecting hidden structures"
    - "sum of squares lower bounds for refuting any csp"
    - "quasi ptas for scheduling with precedences using lp hierarchies"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "on the hardest problem formulations for the 0 1 lasserre hierarchy"

Connected through hyperedge HE-3 (induced by source paper "flow time optimization for concurrent open shop and precedence constrained scheduling models"):
  with members:
    - "scheduling to minimize total weighted completion time via time indexed linear programming relaxations"
    - "an improved bound for minimizing the total weighted completion time of coflows in datacenters"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints"
    - "selfishmigrate a scalable algorithm for non clairvoyantly scheduling heterogeneous processors"
    - "the geometry of scheduling"

Connected through hyperedge HE-4 (induced by source paper "scheduling to minimize total weighted completion time via time indexed linear programming relaxations"):
  with members:
    - "better unrelated machine scheduling for weighted completion time via random offsets from non uniform distributions"
    - "a 2 542 approximation for precedence constrained single machine scheduling with release dates and total weighted completion time objective"
    - "on the configuration lp of the restricted assignment problem"
    - "lift and round to improve weighted completion time on unrelated machines"
    - "towards tight lower bounds for scheduling problems"
    - "santa claus schedules jobs on unrelated machines"
    - "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)