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 "frameworks for designing in place graph algorithms"):
  Abstract: Read-only memory model is a classical model of computation to study time-space tradeoffs of algorithms. One of the classical results on the ROM model is that any sorting algorithm that uses O(s) words of extra space requires $\Omega (n^2/s)$ comparisons for $ \lg n \leq s \leq n/\lg n$ and the bound has also been recently matched by an algorithm. However, if we relax the model (from ROM), we do have sorting algorithms (say Heapsort) that can sort using $O(n \lg n)$ comparisons using $O(\lg n)$ bits of extra space, even keeping a permutation of the given input sequence at any point of time during the algorithm. #R##N#We address similar questions for graph algorithms. We show that a simple natural relaxation of ROM model allows us to implement fundamental graph search methods like BFS and DFS more space efficiently than in ROM. By simply allowing elements in the adjacency list of a vertex to be permuted, we show that, on an undirected or directed connected graph $G$ having $n$ vertices and $m$ edges, the vertices of $G$ can be output in a DFS or BFS order using $O(\lg n)$ bits of extra space and $O(n^3 \lg n)$ time. Thus we obtain similar bounds for reachability and shortest path distance (both for undirected and directed graphs). With a little more (but still polynomial) time, we can also output vertices in the lex-DFS order. As reachability in directed graphs and shortest path distance are NL-complete, and lex-DFS is P-complete, our results show that our model is more powerful than ROM if L $\neq$ P. En route, we also introduce and develop algorithms for another relaxation of ROM where the adjacency lists of the vertices are circular lists and we can modify only the heads of the lists. All our algorithms are simple but quite subtle, and we believe that these models are practical enough to spur interest for other graph problems in these models.

Includes members:
  Member "biconnectivity st numbering and other applications of dfs using o n bits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "indexing graph search trees and applications")
  Member "succinct choice dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "space efficient vertex separators for treewidth")
  Member "canonizing graphs of bounded tree width in logspace":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph isomorphism in quasipolynomial time parameterized by treewidth")
  Member "exact algorithms via monotone local search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hitting topological minors is fpt")
  Member "selection from read only memory with limited workspace":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improved space efficient algorithms for bfs dfs and applications")
  Member "the isomorphism problem for planar 3 connected graphs is in unambiguous logspace":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a log space algorithm for canonization of planar graphs")
  Member "radix sorting with no extra space":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "partial sums on the ultra wide word ram")

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)