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: "entropy bounds for grammar compression"
  Abstract: In grammar compression we represent a string as a context free grammar. This model is popular both in theoretical and practical applications due to its simplicity, good compression rate and suitability for processing of the compressed representations. In practice, achieving compression requires encoding such grammar as a binary string, there are a few commonly used. We bound the size of such encodings for several compression methods, along with well-known \RePair algorithm. For \RePair we prove that its standard encoding, which is a combination of entropy coding and special encoding of a grammar, achieves $1.5|S|H_k(S)$. We also show that by stopping after some iteration we can achieve $|S|H_k(S)$. The latter is particularly important, as it explains the phenomenon observed in practice, that introducing too many nonterminals causes the bit-size to grow. We generalize our approach to other compressions methods like \Greedy or wide class of irreducible grammars, and other bit encodings (including naive, which uses fixed-length codes). Our approach not only proves the bounds but also partially explains why \Greedy and \RePair are much better in practice than the other grammar based methods. At last, we show that for a wide family of dictionary compression methods (including grammar compressors) $\Omega\left(nk \log \sigma/\log_\sigma n\right)$ bits of redundancy are required. This shows a separation between context-based/BWT methods and dictionary compression algorithms, as for the former there exists methods where redundancy does not depend on $n$, but only on $k$~and~$\sigma$.

Connected through hyperedge HE-1 (induced by source paper "entropy bounds for grammar based tree compressors"):
  with members:
    - "grammar based compression of unranked trees"
    - "using statistical encoding to achieve tree succinctness never seen before"
    - "a universal tree balancing theorem"
    - "universal tree source coding using grammar based compression"
    - "a universal grammar based code for lossless compression of binary trees"
    - "large alphabets and incompressibility"
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-2 (induced by source paper "the smallest grammar problem revisited"):
  with members:
    - "at the roots of dictionary compression string attractors"
    - "a succinct grammar compression"
    - "approximation of grammar based compression via recompression"
    - "identifying hierarchical structure in sequences a linear time algorithm"

Connected through hyperedge HE-3 (induced by source paper "towards better compressed representations"):
  with members:
    - "optimal dynamic sequence representations"
    - "cram compressed random access memory"

Connected through hyperedge HE-4 (induced by source paper "repair in compressed space and time"):
  with members:
    - "approximation ratio of repair"
    - "practical and effective re pair compression"
    - "approximation of smallest linear tree grammar"
    - "restructuring compressed texts without explicit decompression"

Connected through hyperedge HE-5 (induced by source paper "using statistical encoding to achieve tree succinctness never seen before"):
  with members:
    - "optimal time text indexing in bwt runs bounded space"
    - "tree compression with top trees"
    - "optimal lower and upper bounds for representing sequences"

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)