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: "towards intelligent compression in streams a biased reservoir sampling based bloom filter approach"
  Abstract: With the explosion of information stored world-wide,data intensive computing has become a central area of research.Efficient management and processing of this massively exponential amount of data from diverse sources,such as telecommunication call data records,online transaction records,etc.,has become a necessity.Removing redundancy from such huge(multi-billion records) datasets resulting in resource and compute efficiency for downstream processing constitutes an important area of study. "Intelligent compression" or deduplication in streaming scenarios,for precise identification and elimination of duplicates from the unbounded datastream is a greater challenge given the realtime nature of data arrival.Stable Bloom Filters(SBF) address this problem to a certain extent.However,SBF suffers from a high false negative rate(FNR) and slow convergence rate,thereby rendering it inefficient for applications with low FNR tolerance.In this paper, we present a novel Reservoir Sampling based Bloom Filter,(RSBF) data structure,based on the combined concepts of reservoir sampling and Bloom filters for approximate detection of duplicates in data streams.Using detailed theoretical analysis we prove analytical bounds on its false positive rate(FPR),false negative rate(FNR) and convergence rates with low memory requirements.We show that RSBF offers the currently lowest FN and convergence rates,and are better than those of SBF while using the same memory.Using empirical analysis on real-world datasets(3 million records) and synthetic datasets with around 1 billion records,we demonstrate upto 2x improvement in FNR with better convergence rates as compared to SBF,while exhibiting comparable FPR.To the best of our knowledge,this is the first attempt to integrate reservoir sampling method with Bloom filters for deduplication in streaming scenarios.

Connected through hyperedge HE-1 (induced by source paper "age partitioned bloom filters"):
  with members:
    - "pay for a sliding bloom filter and get counting distinct elements and entropy for free"

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.IR(Information Retrieval)