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: "fast prefix search in little space with applications"
  Abstract: It has been shown in the indexing literature that there is an essential difference between prefix/range searches on the one hand, and predecessor/rank searches on the other hand, in that the former provably allows faster query resolution. Traditionally, prefix search is solved by data structures that are also dictionaries---they actually contain the strings in $S$. For very large collections stored in slow-access memory, we propose much more compact data structures that support \emph{weak} prefix searches---they return the ranks of matching strings provided that \emph{some} string in $S$ starts with the given prefix. In fact, we show that our most space-efficient data structure is asymptotically space-optimal. Previously, data structures such as String B-trees (and more complicated cache-oblivious string data structures) have implicitly supported weak prefix queries, but they all have query time that grows logarithmically with the size of the string collection. In contrast, our data structures are simple, naturally cache-efficient, and have query time that depends only on the length of the prefix, all the way down to constant query time for strings that fit in one machine word. We give several applications of weak prefix searches, including exact prefix counting and approximate counting of tuples matching conjunctive prefix conditions.

Connected through hyperedge HE-1 (induced by source paper "fully functional suffix trees and optimal text searching in bwt runs bounded space"):
  with members:
    - "resolution of the burrows wheeler transform conjecture"
    - "faster repetition aware compressed suffix trees based on block trees"
    - "optimal time dictionary compressed indexes"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "on the approximation ratio of greedy parsings"
    - "universal compressed text indexing"
    - "prefix free parsing for building big bwts"
    - "representing the suffix tree with the cdawg"

Connected through hyperedge HE-2 (induced by source paper "universal compressed text indexing"):
  with members:
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "fast label extraction in the cdawg"
    - "optimal time text indexing in bwt runs bounded space"
    - "sparse suffix tree construction in optimal time and space"
    - "a self index on block trees"
    - "dynamic index and lz factorization in compressed space"

Connected through hyperedge HE-3 (induced by source paper "optimal time dictionary compressed indexes"):
  with members:
    - "optimal rank and select queries on dictionary compressed text"
    - "universal compressed text indexing"
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "at the roots of dictionary compression string attractors"
    - "compressed indexing with signature grammars"
    - "document listing on repetitive collections with guaranteed performance"
    - "optimal time text indexing in bwt runs bounded space"

Connected through hyperedge HE-4 (induced by source paper "document listing on repetitive collections with guaranteed performance"):
  with members:
    - "universal compressed text indexing"
    - "time space trade offs for lempel ziv compressed indexing"
    - "compressed indexing with signature grammars"
    - "optimal time text indexing in bwt runs bounded space"
    - "document retrieval on repetitive string collections"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "document retrieval on repetitive collections"

Connected through hyperedge HE-5 (induced by source paper "fast compressed self indexes with deterministic linear time construction"):
  with members:
    - "linear time string indexing and analysis in small space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "deterministic indexing for packed strings"
    - "linear time construction of compressed text indices in compact space"
    - "suffix trays and suffix trists structures for faster text indexing"
    - "alphabet dependent string searching with wexponential search trees"
    - "optimal lower and upper bounds for representing sequences"
    - "compression via matroids a randomized polynomial kernel for odd cycle transversal"

Connected through hyperedge HE-6 (induced by source paper "time space trade offs for lempel ziv compressed indexing"):
  with members:
    - "universal indexes for highly repetitive document collections"
    - "composite repetition aware data structures"
    - "searching and indexing genomic databases via kernelization"
    - "queries on lz bounded encodings"
    - "orthogonal range searching for text indexing"
    - "time space trade offs for longest common extensions"
    - "orthogonal range searching on the ram revisited"
    - "improved grammar based compressed indexes"

Connected through hyperedge HE-7 (induced by source paper "compressed indexing with signature grammars"):
  with members:
    - "time space trade offs for lempel ziv compressed indexing"
    - "optimal time text indexing in bwt runs bounded space"
    - "longest common extensions with recompression"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "optimal dynamic strings"
    - "orthogonal range searching on the ram revisited"
    - "faster fully compressed pattern matching by recompression"

Connected through hyperedge HE-8 (induced by source paper "fast scalable construction of minimal perfect hash functions"):
  with members:
    - "bubing massive crawling for the masses"
    - "cache oblivious peeling of random hypergraphs"
    - "satisfiability thresholds beyond k xorsat"
    - "sharp load thresholds for cuckoo hashing"
    - "tight thresholds for cuckoo hashing via xorsat"

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)