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: "hardness of approximation for strip packing"
  Abstract: Strip packing is a classical packing problem, where the goal is to pack a set of rectangular objects into a strip of a given width, while minimizing the total height of the packing. The problem has multiple applications, e.g. in scheduling and stock-cutting, and has been studied extensively. #R##N#When the dimensions of objects are allowed to be exponential in the total input size, it is known that the problem cannot be approximated within a factor better than $3/2$, unless $\mathrm{P}=\mathrm{NP}$. However, there was no corresponding lower bound for polynomially bounded input data. In fact, Nadiradze and Wiese [SODA 2016] have recently proposed a $(1.4 + \epsilon)$ approximation algorithm for this variant, thus showing that strip packing with polynomially bounded data can be approximated better than when exponentially large values in the input data are allowed. Their result has subsequently been improved to a $(4/3 + \epsilon)$ approximation by two independent research groups [FSTTCS 2016, arXiv:1610.04430]. This raises a question whether strip packing with polynomially bounded input data admits a quasi-polynomial time approximation scheme, as is the case for related two-dimensional packing problems like maximum independent set of rectangles or two-dimensional knapsack. #R##N#In this paper we answer this question in negative by proving that it is NP-hard to approximate strip packing within a factor better than $12/11$, even when admitting only polynomially bounded input data. In particular, this shows that the strip packing problem admits no quasi-polynomial time approximation scheme, unless $\mathrm{NP} \subseteq \mathrm{DTIME}(2^{\mathrm{polylog}(n)})$.

Connected through hyperedge HE-1 (induced by source paper "closing the gap for pseudo polynomial strip packing"):
  with members:
    - "improved pseudo polynomial time approximation for strip packing"
    - "scheduling monotone moldable jobs in linear time"
    - "complexity and inapproximability results for parallel task scheduling and strip packing"
    - "improved approximation for two dimensional strip packing with polynomial bounded width"
    - "power strip packing of malleable demands in smart grid"

Connected through hyperedge HE-2 (induced by source paper "complexity and inapproximability results for parallel task scheduling and strip packing"):
  with members:
    - "improved pseudo polynomial time approximation for strip packing"
    - "scheduling monotone moldable jobs in linear time"
    - "closing the gap for pseudo polynomial strip packing"
    - "improved approximation for two dimensional strip packing with polynomial bounded width"

Connected through hyperedge HE-3 (induced by source paper "approximating geometric knapsack via l packings"):
  with members:
    - "improved pseudo polynomial time approximation for strip packing"
    - "improved approximation for two dimensional strip packing with polynomial bounded width"
    - "approximation schemes for maximum weight independent set of rectangles"

Connected through hyperedge HE-4 (induced by source paper "improved approximation for two dimensional strip packing with polynomial bounded width"):
  with members:
    - "improved pseudo polynomial time approximation for strip packing"
    - "complexity and inapproximability results for parallel task scheduling and strip packing"
    - "closing the gap for pseudo polynomial strip packing"

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)