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 "a constructive arboricity approximation scheme"):
  Abstract: The arboricity of a graph is the minimum number of forests it can be partitioned into. Previous approximation schemes were nonconstructive, i.e., they only approximated the arboricity as a value without computing a corresponding forest partition, as they operate on the related pseudoforest partitions or the dual problem. #R##N#We propose an algorithm for converting a partition of $k$ pseudoforests into a partition of $k+1$ forests in $O(m(k\alpha(n) + \log n))$ time, where $\alpha$ is the inverse Ackermann function, when $O(m)$ expected time is allowed for pre-computation of a perfect hash function. Without perfect hashing, we obtain $O(m(k\log k + k\alpha(n) + \log n))$ with a data structure by Brodal and Fagerberg that stores graphs of arboricity $k$. #R##N#For every fixed $\epsilon > 0$, the latter result implies a constructive $(1+\epsilon)$-approximation algorithm with runtime $O(m\log n)$ by using Kowalik's approximation scheme for pseudoforest partitions. Our algorithm might help in designing a faster exact arboricity algorithm. #R##N#We also make several remarks on approximation algorithms for the pseudoarboricity and the equivalent graph orientations with smallest maximum indegree, and correct some mistakes made in the literature.

Includes members:
  Member "testing bounded arboricity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved dynamic graph coloring")
  Member "egalitarian graph orientations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "navigating central path with electrical flows from flows to matchings and back":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "listing all maximal cliques in sparse graphs in near optimal time":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "linear time algorithms for finding a dominating set of fixed size in degenerated graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "logarithmic lower bounds in the cell probe model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "the geometric thickness of low degree graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relating graph thickness to planar layers and bend complexity")

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)