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: "data structure lower bounds on random access to grammar compressed strings"
  Abstract: In this paper we investigate the problem of building a static data structure that represents a string s using space close to its compressed size, and allows fast access to individual characters of s. This type of structures was investigated by the recent paper of Bille et al. Let n be the size of a context-free grammar that derives a unique string s of length L. (Note that L might be exponential in n.) Bille et al. showed a data structure that uses space O(n) and allows to query for the i-th character of s using running time O(log L). Their data structure works on a word RAM with a word size of logL bits. Here we prove that for such data structures, if the space is poly(n), then the query time must be at least (log L)^{1-\epsilon}/log S where S is the space used, for any constant eps>0. As a function of n, our lower bound is \Omega(n^{1/2-\epsilon}). Our proof holds in the cell-probe model with a word size of log L bits, so in particular it holds in the word RAM model. We show that no lower bound significantly better than n^{1/2-\epsilon} can be achieved in the cell-probe model, since there is a data structure in the cell-probe model that uses O(n) space and achieves O(\sqrt{n log n}) query time. The "bad" setting of parameters occurs roughly when L=2^{\sqrt{n}}. We also prove a lower bound for the case of not-as-compressible strings, where, say, L=n^{1+\epsilon}. For this case, we prove that if the space is n polylog(n), then the query time must be at least \Omega(log n/loglog n). #R##N#The proof works by reduction to communication complexity, namely to the LSD problem, recently employed by Patrascu and others. We prove lower bounds also for the case of LZ-compression and Burrows-Wheeler (BWT) compression. All of our lower bounds hold even when the strings are over an alphabet of size 2 and hold even for randomized data structures with 2-sided error.

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"
    - "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-2 (induced by source paper "document listing on repetitive collections with guaranteed performance"):
  with members:
    - "fast prefix search in little space with applications"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "optimal time text indexing in bwt runs bounded space"):
  with members:
    - "linear size cdawg new repetition aware indexing and grammar compression"
    - "fast label extraction in the cdawg"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "fully dynamic data structure for lce queries in compressed space"
    - "universal indexes for highly repetitive document collections"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "composite repetition aware data structures"
    - "computing lz77 in run compressed space"

Connected through hyperedge HE-4 (induced by source paper "at the roots of dictionary compression string attractors"):
  with members:
    - "representing the suffix tree with the cdawg"
    - "time space trade offs for lempel ziv compressed indexing"
    - "fast label extraction in the cdawg"
    - "optimal time text indexing in bwt runs bounded space"
    - "space efficient construction of compressed indexes in deterministic linear time"
    - "fully dynamic data structure for lce queries in compressed space"
    - "composite repetition aware data structures"
    - "linear time construction of compressed text indices in compact space"

Connected through hyperedge HE-5 (induced by source paper "grammar compressed sequences with rank select support"):
  with members:
    - "queries on lz bounded encodings"
    - "spaces trees and colors the algorithmic landscape of document retrieval on sequences"
    - "a succinct grammar compression"
    - "compact binary relation representations with rich functionality"
    - "optimal lower and upper bounds for representing sequences"
    - "on compressing permutations and adaptive sorting"
    - "optimal trade off for succinct string indexes"
    - "efficient fully compressed sequence representations"

Connected through hyperedge HE-6 (induced by source paper "representing the suffix tree with the cdawg"):
  with members:
    - "large alphabets and incompressibility"

Connected through hyperedge HE-7 (induced by source paper "finger search in grammar compressed strings"):
  with members:
    - "fully dynamic data structure for lce queries in compressed space"
    - "dynamic index lz factorization and lce queries in compressed space"
    - "queries on lz bounded encodings"
    - "compressed subsequence matching and packed tree coloring"
    - "computing convolution on grammar compressed text"
    - "fingerprints in compressed strings"
    - "a faster grammar based self index"
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-8 (induced by source paper "finger search random access and longest common extensions in grammar compressed strings"):
  with members:
    - "fingerprints in compressed strings"

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.CC(Computational Complexity)