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: "fixed block compression boosting in fm indexes"
  Abstract: A compressed full-text self-index occupies space close to that of the compressed text and simultaneously allows fast pattern matching and random access to the underlying text. Among the best compressed self-indexes, in theory and in practice, are several members of the FM-index family. In this paper, we describe new FM-index variants that combine nice theoretical properties, simple implementation and improved practical performance. Our main result is a new technique called fixed block compression boosting, which is a simpler and faster alternative to optimal compression boosting and implicit compression boosting used in previous FM-indexes.

Connected through hyperedge HE-1 (induced by source paper "compressed multiple pattern matching"):
  with members:
    - "greedy shortest common superstring approximation in compact space"
    - "dictionary matching in a stream"
    - "engineering small space dictionary matching"
    - "succinct dictionary matching with no slowdown"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-2 (induced by source paper "cinct compression and retrieval for massive vehicular trajectories via relative movement labeling"):
  with members:
    - "csa fast pattern search for large alphabets"
    - "compact trip representation over networks"
    - "press a novel framework of trajectory compression in road networks"
    - "on compressing permutations and adaptive sorting"

Connected through hyperedge HE-3 (induced by source paper "relative compressed suffix trees"):
  with members:
    - "indexing variation graphs"
    - "rlzap relative lempel ziv with adaptive pointers"
    - "composite repetition aware data structures"
    - "searching and indexing genomic databases via kernelization"
    - "from theory to practice plug and play with succinct data structures"
    - "a faster grammar based self index"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-4 (induced by source paper "fm index for dummies"):
  with members:
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "two simple full text indexes based on the suffix array"
    - "sampling the suffix array with minimizers"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-5 (induced by source paper "efficient and compact representations of prefix codes"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "compressed representations of permutations and applications"
    - "fast and compact prefix codes"
    - "worst case optimal adaptive prefix coding"
    - "dynamic asymmetric communication"
    - "large alphabets and incompressibility"
    - "time space trade offs for predecessor search"

Connected through hyperedge HE-6 (induced by source paper "two simple full text indexes based on the suffix array"):
  with members:
    - "alphabet dependent string searching with wexponential search trees"
    - "large scale pattern search using reduced space on disk suffix arrays"
    - "compressed text indexes from theory to practice"

Connected through hyperedge HE-7 (induced by source paper "optimal dynamic sequence representations"):
  with members:
    - "new lower and upper bounds for representing sequences"
    - "a dynamic stabbing max data structure with sub logarithmic query time"
    - "succinct representations of dynamic strings"
    - "compressed representations of permutations and applications"
    - "fully functional static and dynamic succinct trees"

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)