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: "adaptive cuckoo filters"
  Abstract: We introduce the adaptive cuckoo filter (ACF), a data structure for approximate set membership that extends cuckoo filters by reacting to false positives, removing them for future queries. As an example application, in packet processing queries may correspond to flow identifiers, so a search for an element is likely to be followed by repeated searches for that element. Removing false positives can therefore significantly lower the false positive rate. The ACF, like the cuckoo filter, uses a cuckoo hash table to store fingerprints. We allow fingerprint entries to be changed in response to a false positive in a manner designed to minimize the effect on the performance of the filter. We show that the ACF is able to significantly reduce the false positive rate by presenting both a theoretical model for the false positive rate and simulations using both synthetic data sets and real packet traces.

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"
    - "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"
    - "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-2 (induced by source paper "a model for learned bloom filters and optimizing by sandwiching"):
  with members:
    - "optimizing learned bloom filters by sandwiching"
    - "a model for learned bloom filters and related structures"
    - "the case for learned index structures"
    - "bloom filters in adversarial environments"
    - "bloomier filters a second look"

Connected through hyperedge HE-3 (induced by source paper "concurrent expandable amqs on the basis of quotient filters"):
  with members:
    - "concurrent hash tables fast and general"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-4 (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"
    - "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"
    - "don t thrash how to cache your hash on flash"

Connected through hyperedge HE-5 (induced by source paper "a model for learned bloom filters and related structures"):
  with members:
    - "the case for learned index structures"

Connected through hyperedge HE-6 (induced by source paper "bloom filters adaptivity and the dictionary problem"):
  with members:
    - "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"
    - "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.DS(Data Structures and Algorithms)