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: "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.

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"
    - "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 "bloom filters in adversarial environments"):
  with members:
    - "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"
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"

Connected through hyperedge HE-3 (induced by source paper "approximate range emptiness in constant time and optimal space"):
  with members:
    - "fast prefix search in little space with applications"

Connected through hyperedge HE-4 (induced by source paper "sliding bloom filters"):
  with members:
    - "backyard cuckoo hashing constant worst case operations with a succinct representation"
    - "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)