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 "optimizing bloom filter challenges solutions and comparisons"):
  Abstract: Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

Includes members:
  Member "a survey on security and privacy issues of bitcoin":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the feasibility of sybil attacks in shard based permissionless blockchains")
  Member "a model for learned bloom filters and related structures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning neural bloom filters")
  Member "the case for learned index structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "adaptive cuckoo filters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  Member "location privacy preservation in database driven wireless cognitive networks through encrypted probabilistic data structures":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "location privacy in cognitive radios with multi server private information retrieval")
  Member "cuckoo filter simplification and analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "answering spatial multiple set intersection queries using 2 3 cuckoo hash filters")
  Member "yes no bloom filter a way of representing sets with fewer false positives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autoscaling bloom filter controlling trade off between true and false positives")
  Member "a shifting bloom filter framework for set queries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bloom filter variants for multiple sets a comparative assessment")

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)