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: "optimal lower and upper bounds for representing sequences"
  Abstract: Sequence representations supporting queries $access$, $select$ and $rank$ are at the core of many data structures. There is a considerable gap between the various upper bounds and the few lower bounds known for such representations, and how they relate to the space used. In this article we prove a strong lower bound for $rank$, which holds for rather permissive assumptions on the space used, and give matching upper bounds that require only a compressed representation of the sequence. Within this compressed space, operations $access$ and $select$ can be solved in constant or almost-constant time, which is optimal for large alphabets. Our new upper bounds dominate all of the previous work in the time/space map.

Connected through hyperedge HE-1 (induced by source paper "energy consumption in compact integer vectors a study case"):
  with members:
    - "energy efficient algorithms"
    - "compact binary relation representations with rich functionality"
    - "on compressing permutations and adaptive sorting"
    - "orthogonal range searching on the ram revisited"
    - "succinct representations of permutations and functions"
    - "fully functional static and dynamic succinct trees"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-2 (induced by source paper "the alternating bwt an algorithmic perspective"):
  with members:
    - "a new class of searchable and provably highly compressible string transformations"
    - "on generalized lyndon words"
    - "external memory bwt and lcp computation for sequence collections with applications"
    - "lightweight lcp construction for very large collections of strings"
    - "large scale compression of genomic sequence databases with the burrows wheeler transform"
    - "on optimally partitioning a text to improve its compression"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-3 (induced by source paper "a new class of searchable and provably highly compressible string transformations"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "optimal time text indexing in bwt runs bounded space"
    - "large scale compression of genomic sequence databases with the burrows wheeler transform"
    - "a note on the burrows wheeler transformation"

Connected through hyperedge HE-4 (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"
    - "fast prefix search in little space with applications"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "refining the r index"):
  with members:
    - "conversion from rlbwt to lz77"
    - "fully functional suffix trees and optimal text searching in bwt runs bounded space"
    - "lower bounds for text indexing with mismatches and differences"
    - "efficient construction of a complete index for pan genomics read alignment"
    - "prefix free parsing for building big bwts"
    - "from lz77 to the run length encoded burrows wheeler transform and back"
    - "a framework of dynamic data structures for string processing"
    - "optimal construction of compressed indexes for highly repetitive texts"

Connected through hyperedge HE-6 (induced by source paper "on the approximation ratio of greedy parsings"):
  with members:
    - "optimal time dictionary compressed indexes"
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "optimal time text indexing in bwt runs bounded space"
    - "a separation between run length slps and lz77"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "factorizations of the fibonacci infinite word"

Connected through hyperedge HE-7 (induced by source paper "compressed dynamic range majority and minority data structures"):
  with members:
    - "optimal query time for encoding range majority"
    - "range majorities and minorities in arrays"
    - "compressed dynamic range majority data structures"
    - "compressed data structures for dynamic sequences"
    - "optimal encodings for range majority queries"
    - "better space bounds for parameterized range majority and minority"
    - "dynamic range majority data structures"
    - "succinct representations of dynamic strings"

Connected through hyperedge HE-8 (induced by source paper "using statistical encoding to achieve tree succinctness never seen before"):
  with members:
    - "entropy bounds for grammar compression"
    - "optimal time text indexing in bwt runs bounded space"
    - "tree compression with top 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)