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 "how to approximate a set without knowing its size in advance"):
  Abstract: The dynamic approximate membership problem asks to represent a set S of size n, whose elements are provided in an on-line fashion, supporting membership queries without false negatives and with a false positive rate at most epsilon. That is, the membership algorithm must be correct on each x in S, and may err with probability at most epsilon on each x not in S. #R##N#We study a well-motivated, yet insufficiently explored, variant of this problem where the size n of the set is not known in advance. Existing optimal approximate membership data structures require that the size is known in advance, but in many practical scenarios this is not a realistic assumption. Moreover, even if the eventual size n of the set is known in advance, it is desirable to have the smallest possible space usage also when the current number of inserted elements is smaller than n. Our contribution consists of the following results: #R##N#- We show a super-linear gap between the space complexity when the size is known in advance and the space complexity when the size is not known in advance. #R##N#- We show that our space lower bound is tight, and can even be matched by a highly efficient data structure.

Includes members:
  Member "sliding bloom filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "give me some slack efficient network measurements")
  Member "backyard cuckoo hashing constant worst case operations with a succinct representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully dynamic space efficient dictionaries and filters with constant number of memory accesses")
  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")

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)