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 "backyard cuckoo hashing constant worst case operations with a succinct representation"):
  Abstract: The performance of a dynamic dictionary is measured mainly by its update time, lookup time, and space consumption. In terms of update time and lookup time there are known constructions that guarantee constant-time operations in the worst case with high probability, and in terms of space consumption there are known constructions that use essentially optimal space. However, although the first analysis of a dynamic dictionary dates back more than 45 years ago (when Knuth analyzed linear probing in 1963), the trade-off between these aspects of performance is still not completely understood. In this paper we settle two fundamental open problems: - We construct the first dynamic dictionary that enjoys the best of both worlds: it stores n elements using (1+epsilon)n memory words, and guarantees constant-time operations in the worst case with high probability. Specifically, for any epsilon = \Omega((\log\log n / \log n)^{1/2} ) and for any sequence of polynomially many operations, with high probability over the randomness of the initialization phase, all operations are performed in constant time which is independent of epsilon. The construction is a two-level variant of cuckoo hashing, augmented with a "backyard" that handles a large fraction of the elements, together with a de-amortized perfect hashing scheme for eliminating the dependency on epsilon. - We present a variant of the above construction that uses only (1+o(1))B bits, where B is the information-theoretic lower bound for representing a set of size n taken from a universe of size u, and guarantees constant-time operations in the worst case with high probability, as before. This problem was open even in the amortized setting. One of the main ingredients of our construction is a permutation-based variant of cuckoo hashing, which significantly improves the space consumption of cuckoo hashing when dealing with a rather small universe.

Includes members:
  Member "sharp load thresholds for cuckoo hashing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")
  Member "tight thresholds for cuckoo hashing via xorsat":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")
  Member "de amortized cuckoo hashing provable worst case performance and experimental results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "succinct data structures for retrieval and approximate membership":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning neural bloom filters")
  Member "an optimal bloom filter replacement based on matrix solving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "efficient hashing with lookups in two memory accesses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")

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)