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: "peeling close to the orientability threshold spatial coupling in hashing based data structures"
  Abstract: Hypergraphs with random hyperedges underlie various data structures where hash functions map inputs to hyperedges, e.g. cuckoo hash tables, invertible Bloom lookup tables, retrieval data structures and perfect hash functions. #R##N#High memory efficiency and quick query times call for high hyperedge density and small hyperedge size. Moreover, orientability or even peelability of the hypergraph is required or advantageous. #R##N#For $k$-uniform fully random hypergraphs, the thresholds $c_{k,\ell}^*$ for $\ell$-orientability significantly exceed the thresholds for $\ell$-peelability. In this paper, for every $k \geq 2$ and $\ell \geq 1$ with $(k,\ell) \neq (2,1)$ and every $z > 0$, we construct a new family of random $k$-uniform hypergraphs with i.i.d. random hyperedges such that both the $\ell$-peelability and the $\ell$-orientability thresholds approach $c_{k,\ell}^*$ as $z \rightarrow \infty$. #R##N#Our construction is simple: The $N$ vertices are linearly ordered and each hyperedge selects its $k$ elements uniformly at random from a random range of $\frac{N}{z}$ consecutive vertices. #R##N#We thus exploit the phenomenon of threshold saturation via spatial coupling discovered in the context of low density parity check codes. Once the connection to data structures is in plain sight, we employ a framework by Kudekar, Richardson and Urbanke (2015) to do the heavy lifting in our proof. #R##N#We demonstrate the usefulness of our construction, using our hypergraphs as a drop-in replacement in a retrieval data structure by Botelho et al. (2013). This reduces memory usage from $1.23m$ bits to $1.12m$ bits (for input size $m$) with no downsides.

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)