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: "lzrr lz77 parsing with right reference"
  Abstract: Lossless data compression has been widely studied in computer science. One of the most widely used lossless data compressions is Lempel-Zip(LZ) 77 parsing, which achieves a high compression ratio. Bidirectional (a.k.a. macro) parsing is a lossless data compression and computes a sequence of phrases copied from another substring (target phrase) on either the left or the right position in an input string. Gagie et al.(LATIN 2018) recently showed that a large gap exists between the number of smallest bidirectional phrases of a given string and that of LZ77 phrases. In addition, finding the smallest bidirectional parse of a given text is NP-complete. Several variants of bidirectional parsing have been proposed thus far, but no prior work for bidirectional parsing has achieved high compression that is smaller than that of LZ77 phrasing for any string. In this paper, we present the first practical bidirectional parsing named LZ77 parsing with right reference (LZRR), in which the number of LZRR phrases is theoretically guaranteed to be smaller than the number of LZ77 phrases. Experimental results using benchmark strings show the number of LZRR phrases is approximately five percent smaller than that of LZ77 phrases.

Connected through hyperedge HE-1 (induced by source paper "bidirectional text compression in external memory"):
  with members:
    - "o n log n time text compression by lz style longest first substitution"
    - "at the roots of dictionary compression string attractors"
    - "compression with the tudocomp framework"
    - "lempel ziv decoding in external memory"
    - "lz end parsing in compressed space"
    - "lightweight lempel ziv parsing"
    - "lempel ziv parsing in external memory"
    - "simpler and faster lempel ziv factorization"

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)