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: "linear time compression of bounded genus graphs into information theoretically optimal number of bits"
  Abstract: A compression scheme $A$ for a class $\mathbb{G}$ of graphs consists of an encoding algorithm ${\em Encode}_A$ that computes a binary string ${\em Code}_A(G)$ for any given graph $G$ in $\mathbb{G}$ and a decoding algorithm ${\em Decode}_A$ that recovers $G$ from ${\em Code}_A(G)$. A compression scheme $A$ for $\mathbb{G}$ is optimal if both ${\em Encode}_A$ and ${\em Decode}_A$ run in linear time and the number of bits of ${\em Code}_A(G)$ for any $n$-node graph $G$ in $\mathbb{G}$ is information-theoretically optimal to within lower-order terms. Trees and plane triangulations were the only known nontrivial graph classes to admit optimal compression schemes. Based upon Goodrich's separator decomposition for planar graphs and Djidjev and Venkatesan's planarizers for bounded-genus graphs, we give an optimal compression scheme for any hereditary (i.e., closed under taking subgraphs) class $\mathbb{G}$ under the premise that any $n$-node graph of $\mathbb{G}$ to be encoded comes with a genus-$o(\frac{n}{\log...

Connected through hyperedge HE-1 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations"):
  with members:
    - "grammar based graph compression"
    - "compressing networks with super nodes"
    - "graphzip dictionary based compression for mining graph streams"
    - "parallel construction of compact planar embeddings"
    - "compressed representation of dynamic binary relations with applications"
    - "a succinct data structure for self indexing ternary relations"
    - "fully dynamic de bruijn graphs"
    - "compressing graphs and indexes with recursive graph bisection"

Connected through hyperedge HE-2 (induced by source paper "orderly spanning trees with applications"):
  with members:
    - "improved compact visibility representation of planar graph via schnyder s realizer"
    - "compact floor planning via orderly spanning trees"
    - "compact encodings of planar graphs via canonical orderings and multiple parentheses"
    - "linear time succinct encodings of planar graphs via canonical orderings"
    - "a fast general methodology for information theoretically optimal encodings of graphs"

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)