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 "autoscaling bloom filter controlling trade off between true and false positives"):
  Abstract: A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate.

Includes members:
  Member "a theory of sequence indexing and working memory in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperembed tradeoffs between resources and performance in nlp tasks with hyperdimensional computing enabled embedding of n gram statistics")
  Member "theory of the superposition principle for randomized connectionist representations in neural networks":
    also connected through other hyperedges:
      (none)
  Member "neural distributed autoassociative memories a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vector symbolic architectures for context free grammars")
  Member "yes no bloom filter a way of representing sets with fewer false positives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing bloom filter challenges solutions and comparisons")
  Member "don t thrash how to cache your hash on flash":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lsm based storage techniques a survey")
  Member "the deletable bloom filter a new member of the bloom family":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimizing bloom filter challenges solutions and comparisons")

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)