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: "don t thrash how to cache your hash on flash"
  Abstract: This paper presents new alternatives to the well-known Bloom filter data structure. The Bloom filter, a compact data structure supporting set insertion and membership queries, has found wide application in databases, storage systems, and networks. Because the Bloom filter performs frequent random reads and writes, it is used almost exclusively in RAM, limiting the size of the sets it can represent. This paper first describes the quotient filter, which supports the basic operations of the Bloom filter, achieving roughly comparable performance in terms of space and time, but with better data locality. Operations on the quotient filter require only a small number of contiguous accesses. The quotient filter has other advantages over the Bloom filter: it supports deletions, it can be dynamically resized, and two quotient filters can be efficiently merged. The paper then gives two data structures, the buffered quotient filter and the cascade filter, which exploit the quotient filter advantages and thus serve as SSD-optimized alternatives to the Bloom filter. The cascade filter has better asymptotic I/O performance than the buffered quotient filter, but the buffered quotient filter outperforms the cascade filter on small to medium data sets. Both data structures significantly outperform recently-proposed SSD-optimized Bloom filter variants, such as the elevator Bloom filter, buffered Bloom filter, and forest-structured Bloom filter. In experiments, the cascade filter and buffered quotient filter performed insertions 8.6-11 times faster than the fastest Bloom filter variant and performed lookups 0.94-2.56 times faster.

Connected through hyperedge HE-1 (induced by source paper "lsm based storage techniques a survey"):
  with members:
    - "efficient data ingestion and query processing for lsm based storage systems"
    - "asterixdb a scalable open source bdms"
    - "bigtable merge compaction"
    - "optimal aggregation algorithms for middleware"

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"
    - "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"
    - "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 "autoscaling bloom filter controlling trade off between true and false positives"):
  with members:
    - "a theory of sequence indexing and working memory in recurrent neural networks"
    - "theory of the superposition principle for randomized connectionist representations in neural networks"
    - "neural distributed autoassociative memories a survey"
    - "yes no bloom filter a way of representing sets with fewer false positives"
    - "the deletable bloom filter a new member of the bloom family"

Connected through hyperedge HE-4 (induced by source paper "data structures to represent sets of k long dna sequences"):
  with members:
    - "fast and scalable minimal perfect hashing for massive key sets"
    - "fully dynamic de bruijn graphs"
    - "variable order de bruijn graphs"

Connected through hyperedge HE-5 (induced by source paper "concurrent expandable amqs on the basis of quotient filters"):
  with members:
    - "adaptive cuckoo filters"
    - "concurrent hash tables fast and general"

Connected through hyperedge HE-6 (induced by source paper "optimizing bloom filter challenges solutions and comparisons"):
  with members:
    - "a survey on security and privacy issues of bitcoin"
    - "a model for learned bloom filters and related structures"
    - "the case for learned index structures"
    - "adaptive cuckoo filters"
    - "location privacy preservation in database driven wireless cognitive networks through encrypted probabilistic data structures"
    - "cuckoo filter simplification and analysis"
    - "yes no bloom filter a way of representing sets with fewer false positives"
    - "a shifting bloom filter framework for set queries"

Connected through hyperedge HE-7 (induced by source paper "the online event detection problem"):
  with members:
    - "optimal hashing in external memory"
    - "an optimal algorithm for l1 heavy hitters in insertion streams and related problems"
    - "bptree an ell_2 heavy hitters algorithm using constant memory"
    - "heavy hitters via cluster preserving clustering"
    - "beating countsketch for heavy hitters in insertion streams"
    - "communication complexity for algorithm designers"

Connected through hyperedge HE-8 (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"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "an optimal bloom filter replacement based on matrix solving"

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.DB(Databases)