human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "dense peelable random uniform hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "constructing minimal perfect hash functions using sat technology")
  Member "fast scalable construction of minimal perfect hash functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "bubing massive crawling for the masses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "2ps high quality edge partitioning with two phase streaming")
  Member "the multiple orientability thresholds for random hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dense peelable random uniform hypergraphs")
  Member "on the insertion time of random walk cuckoo hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "load thresholds for cuckoo hashing with overlapping blocks")
  Member "high speed hashing for integers and strings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast hashing with strong concentration bounds")
  Member "cache oblivious peeling of random hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense peelable random uniform hypergraphs")
  Member "thresholds of spatially coupled systems via lyapunov s method":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel")

Question: Please tell me which class the source paper 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)