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: "a practical index for approximate dictionary matching with few mismatches"
  Abstract: Approximate dictionary matching is a classic string matching problem, with applications in, e.g., online catalogs, geolocation (mapping possibly misspelled location description to geocoordinates), web searchers, etc. We present a surprisingly simple solution, based on the Dirichlet principle, for matching a keyword with few mismatches and experimentally show that it offers competitive space-time tradeoffs. Our implementation in the C++ language is focused mostly on data compaction, which is beneficial for the search speed (e.g., by being cache friendly). We compare our solution with other algorithms and we show that it performs better for the Hamming distance. Query times in the order of 1--2 microseconds were reported for one mismatch for the dictionary size of a few megabytes on a medium-end PC. We also demonstrate that a basic compression technique consisting in character substitution can significantly reduce the index size for the DNA data (below the dictionary size), while still keeping the query time relatively low.

Connected through hyperedge HE-1 (induced by source paper "full text and keyword indexes for string searching"):
  with members:
    - "the k mismatch problem revisited"
    - "new tabulation and sparse dynamic programming based techniques for sequence similarity problems"
    - "from theory to practice plug and play with succinct data structures"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-2 (induced by source paper "a bloated fm index reducing the number of cache misses during the search"):
  with members:
    - "kmc 2 fast and resource frugal k mer counting"
    - "fm index for dummies"

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)