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: "relative lempel ziv factorization for efficient storage and retrieval of web collections"
  Abstract: Compression techniques that support fast random access are a core component of any information system. Current state-of-the-art methods group documents into fixed-sized blocks and compress each block with a general-purpose adaptive algorithm such as GZIP. Random access to a specific document then requires decompression of a block. The choice of block size is critical: it trades between compression effectiveness and document retrieval times. In this paper we present a scalable compression method for large document collections that allows fast random access. We build a representative sample of the collection and use it as a dictionary in a LZ77-like encoding of the rest of the collection, relative to the dictionary. We demonstrate on large collections, that using a dictionary as small as 0.1% of the collection size, our algorithm is dramatically faster than previous methods, and in general gives much better compression.

Connected through hyperedge HE-1 (induced by source paper "lempel ziv like parsing in small space"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "relations between greedy and bit optimal lz77 encodings"
    - "lz end parsing in compressed space"
    - "approximating lz77 via small space multiple pattern matching"
    - "from theory to practice plug and play with succinct data structures"
    - "lempel ziv parsing in external memory"
    - "linear time lempel ziv factorization simple fast small"
    - "large alphabets and incompressibility"

Connected through hyperedge HE-2 (induced by source paper "rlzap relative lempel ziv with adaptive pointers"):
  with members:
    - "relative compressed suffix trees"
    - "gdc 2 compression of large collections of genomes"
    - "bicriteria data compression"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"

Connected through hyperedge HE-3 (induced by source paper "lempel ziv decoding in external memory"):
  with members:
    - "hybrid indexes for repetitive datasets"
    - "lempel ziv parsing in external memory"
    - "faster approximate pattern matching in compressed repetitive texts"
    - "grammar based compression in a streaming model"

Connected through hyperedge HE-4 (induced by source paper "dynamic relative compression dynamic partial sums and substring concatenation"):
  with members:
    - "approximate range emptiness in constant time and optimal space"
    - "dynamic integer sets with optimal rank select and predecessor search"
    - "reference based genome compression"
    - "optimal dynamic sequence representations"
    - "fully functional static and dynamic succinct trees"

Connected through hyperedge HE-5 (induced by source paper "access time tradeoffs in archive compression"):
  with members:
    - "from theory to practice plug and play with succinct data structures"

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)