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 "improved space efficient algorithms for bfs dfs and applications"):
  Abstract: Recent work by Elmasry et al. (STACS 2015) and Asano et al. (ISAAC 2014), reconsidered classical fundamental graph algorithms focusing on improving the space complexity. We continue this line of work focusing on space. Our first result is a simple data structure that can maintain any subset $S$ of a universe of $n$ elements using $n+o(n)$ bits and support in constant time, apart from the standard insert, delete and membership queries, the operation {\it findany} that finds and returns any element of the set (or outputs that the set is empty). Using this we give a BFS implementation that takes $O(m+n)$ time using at most $2n+o(n)$ bits. Later, we further improve the space requirement of BFS to at most $1.585n + o(n)$ bits. We demonstrate the use of our data structure by developing another data structure using it that can represent a sequence of $n$ non-negative integers $x_1, x_2, \ldots x_n$ using at most $\sum_{i=1}^n x_i + 2n + o(\sum_{i=1}^n x_i+n)$ bits and, in constant time, determine whether the $i$-th element is $0$ or decrement it otherwise. We also discuss an algorithm for finding a minimum weight spanning tree of a weighted undirected graph using at most $n+o(n)$ bits. For DFS, we have two kinds of results. Specifically, we provide an implementation for DFS that takes $O(m+n)$ time using $O(m+n)$ bits. Using the DFS algorithm and other careful implementations, we can determine cut vertices, bridges and maximal 2-connected components among others, in $O(m+n)$ time and $O(m+n)$ bits. We also present space efficient implementations for finding strongly connected components, topological sort, and a sparse biconnected subgraph of a biconnected graph using $O(n)$ bits. These improve the space required for earlier implementations from $\Omega (n\lg n)$ bits.

Includes members:
  Member "succinct choice dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "space efficient vertex separators for treewidth")
  Member "selection from read only memory with limited workspace":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "frameworks for designing in place graph algorithms")
  Member "a simple test on 2 vertex and 2 edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a simple certifying algorithm for 3 edge connectivity")
  Member "space time trade offs for stack based algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "common tangents of two disjoint polygons in linear time and constant workspace")
  Member "fully functional static and dynamic succinct trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully functional bidirectional burrows wheeler indexes")
  Member "an o m algorithm for cores decomposition of networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")

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)