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: "fast scalable construction of minimal perfect hash functions"
  Abstract: Recent advances in random linear systems on finite fields have paved the way for the construction of constant-time data structures representing static functions and minimal perfect hash functions using less space with respect to existing techniques. The main obstruction for any practical application of these results is the cubic-time Gaussian elimination required to solve these linear systems: despite they can be made very small, the computation is still too slow to be feasible. #R##N#In this paper we describe in detail a number of heuristics and programming techniques to speed up the resolution of these systems by several orders of magnitude, making the overall construction competitive with the standard and widely used MWHC technique, which is based on hypergraph peeling. In particular, we introduce broadword programming techniques for fast equation manipulation and a lazy Gaussian elimination algorithm. We also describe a number of technical improvements to the data structure which further reduce space usage and improve lookup speed. #R##N#Our implementation of these techniques yields a minimal perfect hash function data structure occupying 2.24 bits per element, compared to 2.68 for MWHC-based ones, and a static function data structure which reduces the multiplicative overhead from 1.23 to 1.03.

Connected through hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "bubing massive crawling for the masses"
    - "the multiple orientability thresholds for random hypergraphs"
    - "on the insertion time of random walk cuckoo hashing"
    - "high speed hashing for integers and strings"
    - "cache oblivious peeling of random hypergraphs"
    - "thresholds of spatially coupled systems via lyapunov s method"
    - "a simple proof of threshold saturation for coupled scalar recursions"

Connected through hyperedge HE-2 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses"):
  with members:
    - "bloom filters adaptivity and the dictionary problem"
    - "adaptive cuckoo filters"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "how to approximate a set without knowing its size in advance"
    - "don t thrash how to cache your hash on flash"
    - "the power of simple tabulation hashing"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"

Connected through hyperedge HE-3 (induced by source paper "dense peelable random uniform hypergraphs"):
  with members:
    - "the multiple orientability thresholds for random hypergraphs"
    - "cache oblivious peeling of random hypergraphs"
    - "biff bloom filter codes fast error correction for large data sets"
    - "invertible bloom lookup tables"
    - "sharp load thresholds for cuckoo hashing"
    - "bloomier filters a second look"
    - "straggler identification in round trip data streams via newton s identities and invertible bloom filters"

Connected through hyperedge HE-4 (induced by source paper "constructing minimal perfect hash functions using sat technology"):
  with members:
    - "dense peelable random uniform hypergraphs"
    - "fast and scalable minimal perfect hashing for massive key sets"
    - "succinct data structures for retrieval and approximate membership"

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)