human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "grammar based graph compression"):
  Abstract: We present a new graph compressor that works by recursively detecting repeated substructures and representing them through grammar rules. We show that for a large number of graphs the compressor obtains smaller representations than other approaches. Specific queries such as reachability between two nodes or regular path queries can be evaluated in linear time (or quadratic times, respectively), over the grammar, thus allowing speed-ups proportional to the compression ratio.

Includes members:
  Member "a succinct data structure for self indexing ternary relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extending general compact querieable representations to gis applications")
  Member "traversing grammar compressed trees with constant delay":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast label extraction in the cdawg")
  Member "compressed tree canonization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grammar based compression of unranked trees")
  Member "recompression a simple and powerful technique for word equations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on string constraint solving")
  Member "fast and tiny structural self indexes for xml":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constant delay traversal of grammar compressed graphs with bounded rank")
  Member "structure induction by lossless graph compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations")
  Member "approximation algorithms for pspace hard hierarchically and periodically specified problems":
    also connected through other hyperedges:
      (none)
  Member "identifying hierarchical structure in sequences a linear time algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")

Question: Please tell me which class the source paper 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)