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: "csa fast pattern search for large alphabets"
  Abstract: Indexed pattern search in text has been studied for many decades. For small alphabets, the FM-Index provides unmatched performance, in terms of both space required and search speed. For large alphabets -- for example, when the tokens are words -- the situation is more complex, and FM-Index representations are compact, but potentially slow. In this paper we apply recent innovations from the field of inverted indexing and document retrieval to compressed pattern search, including for alphabets into the millions. Commencing with the practical compressed suffix array structure developed by Sadakane, we show that the Elias-Fano code-based approach to document indexing can be adapted to provide new tradeoff options in indexed pattern search, and offers significantly faster pattern processing compared to previous implementations, as well as reduced space requirements. We report a detailed experimental evaluation that demonstrates the relative advantages of the new approach, using the standard Pizza&Chili methodology and files, as well as applied use-cases derived from large-scale data compression, and from natural language processing. For large alphabets, the new structure gives rise to space requirements that are close to those of the most highly-compressed FM-Index variants, in conjunction with unparalleled search throughput rates.

Connected through hyperedge HE-1 (induced by source paper "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"):
  with members:
    - "compact trip representation over networks"
    - "press a novel framework of trajectory compression in road networks"
    - "on compressing permutations and adaptive sorting"
    - "fixed block compression boosting in fm indexes"

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)