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: "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.

Connected through hyperedge HE-1 (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"
    - "fast scalable construction of minimal perfect hash functions"
    - "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"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"

Connected through hyperedge HE-2 (induced by source paper "bloom filters adaptivity and the dictionary problem"):
  with members:
    - "adaptive cuckoo filters"
    - "fast and powerful hashing using tabulation"
    - "bloom filters in adversarial environments"
    - "how robust are linear sketches to adaptive inputs"
    - "don t thrash how to cache your hash on flash"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-3 (induced by source paper "fast actively secure ot extension for short secrets"):
  with members:
    - "a new approach to practical active secure two party computation"

Connected through hyperedge HE-4 (induced by source paper "a framework for estimating stream expression cardinalities"):
  with members:
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "bottom k and priority sampling set similarity and subset sums with minimal independence"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"

Connected through hyperedge HE-5 (induced by source paper "triangle counting in dynamic graph streams"):
  with members:
    - "triadic measures on graphs the power of wedge sampling"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "four degrees of separation"
    - "colorful triangle counting and a mapreduce implementation"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-6 (induced by source paper "bloom filters in adversarial environments"):
  with members:
    - "how to approximate a set without knowing its size in advance"
    - "sliding bloom filters"
    - "how robust are linear sketches to adaptive inputs"
    - "explicit and efficient hash families suffice for cuckoo hashing with a stash"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-7 (induced by source paper "how to approximate a set without knowing its size in advance"):
  with members:
    - "sliding bloom filters"
    - "de amortized cuckoo hashing provable worst case performance and experimental results"
    - "succinct data structures for retrieval and approximate membership"
    - "an optimal bloom filter replacement based on matrix solving"

Connected through hyperedge HE-8 (induced by source paper "sliding bloom filters"):
  with members:
    - "how to approximate a set without knowing its size in advance"
    - "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)