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: "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.

Connected through hyperedge HE-1 (induced by source paper "multiset synchronization with counting cuckoo filters"):
  with members:
    - "invertible bloom lookup tables"

Connected through hyperedge HE-2 (induced by source paper "analysis of sparsehash an efficient embedding of set similarity via sparse projections"):
  with members:
    - "binary adaptive embeddings from order statistics of random projections"
    - "fast similarity sketching"
    - "a survey on learning to hash"
    - "exact weighted minwise hashing in constant time"
    - "set similarity search beyond minhash"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "b bit minwise hashing"

Connected through hyperedge HE-3 (induced by source paper "memory efficient performance monitoring on programmable switches with lean algorithms"):
  with members:
    - "constant time updates in hierarchical heavy hitters"
    - "heavy hitter detection entirely in the data plane"
    - "dapper data plane performance diagnosis of tcp"
    - "hierarchical heavy hitters with the space saving algorithm"

Connected through hyperedge HE-4 (induced by source paper "bloom filter variants for multiple sets a comparative assessment"):
  with members:
    - "a shifting bloom filter framework for set queries"
    - "bloomier filters a second look"

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)