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: "compact encodings of planar graphs via canonical orderings and multiple parentheses"
  Abstract: Let G be a plane graph of n nodes, m edges, f faces, and no self-loop. G need not be connected or simple (i.e., free of multiple edges). We give three sets of coding schemes for G which all take O(m+n) time for encoding and decoding. Our schemes employ new properties of canonical orderings for planar graphs and new techniques of processing strings of multiple types of parentheses. #R##N#For applications that need to determine in O(1) time the adjacency of two nodes and the degree of a node, we use 2m+(5+1/k)n + o(m+n) bits for any constant k > 0 while the best previous bound by Munro and Raman is 2m+8n + o(m+n). If G is triconnected or triangulated, our bit count decreases to 2m+3n + o(m+n) or 2m+2n + o(m+n), respectively. If G is simple, our bit count is (5/3)m+(5+1/k)n + o(n) for any constant k > 0. Thus, if a simple G is also triconnected or triangulated, then 2m+2n + o(n) or 2m+n + o(n) bits suffice, respectively. #R##N#If only adjacency queries are supported, the bit counts for a general G and a simple G become 2m+(14/3)n + o(m+n) and (4/3)m+5n + o(n), respectively. #R##N#If we only need to reconstruct G from its code, a simple and triconnected G uses roughly 2.38m + O(1) bits while the best previous bound by He, Kao, and Lu is 2.84m.

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 "fast and compact planar embeddings"):
  with members:
    - "dynamic data structures for document collections and graphs"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "fully functional static and dynamic succinct trees"
    - "succinct indexable dictionaries with applications to encoding k ary trees prefix sums and multisets"
    - "practical entropy compressed rank select dictionary"
    - "orderly spanning trees with applications"
    - "dynamic generators of topologically embedded graphs"
    - "linear time succinct encodings of planar graphs via canonical orderings"

Connected through hyperedge HE-3 (induced by source paper "linear time compression of bounded genus graphs into information theoretically optimal number of bits"):
  with members:
    - "faster approximate distance queries and compact routing in sparse graphs"
    - "orderly spanning trees with applications"
    - "linear time succinct encodings of planar graphs via canonical orderings"
    - "a fast general methodology for information theoretically optimal encodings of graphs"

Connected through hyperedge HE-4 (induced by source paper "orderly spanning trees with applications"):
  with members:
    - "linear time compression of bounded genus graphs into information theoretically optimal number of bits"
    - "improved compact visibility representation of planar graph via schnyder s realizer"
    - "compact floor planning via orderly spanning trees"
    - "linear time succinct encodings of planar graphs via canonical orderings"
    - "a fast general methodology for information theoretically optimal encodings of graphs"

Connected through hyperedge HE-5 (induced by source paper "a fast general methodology for information theoretically optimal encodings of graphs"):
  with members:
    - "linear time succinct encodings of planar graphs via canonical orderings"

Connected through hyperedge HE-6 (induced by source paper "connectivity compression for irregular quadrilateral meshes"):
  with members:
    - "linear time succinct encodings of planar graphs via canonical orderings"
    - "quadrilateral meshing by circle packing"

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)