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 "distributed dense subgraph detection and low outdegree orientation"):
  Abstract: The maximum density subgraph problem, introduced in the 80s by Picard and Queyranne as well as Goldberg, is a classic problem in combinatorial optimization with a wide range of applications. The lowest outdegree orientation problem is known to be its dual problem. We study both the problem of finding dense subgraphs and the problem of computing a low outdegree orientation in the distributed settings. #R##N#Suppose $G=(V,E)$ is the underlying network as well as the input graph. Let $D$ denote the density of the maximum density subgraph of $G$. We give the following results: #R##N#Given a value $\tilde{D} \leq D$ and $0 < \epsilon < 1$, we show that a subgraph of density at least $(1-\epsilon)\tilde{D}$ can be identified deterministically in $O((\log n) / \epsilon)$ rounds in the \textsf{LOCAL} model. Using randomization, we show that such subgraph can be identified in $O((\log^3 n) / \epsilon^3)$ rounds in the \textsf{CONGEST} model with high probability. We also give a $\Omega(1/\epsilon)$-round lower bound which shows that our result for the \textsf{LOCAL} model is tight up to a $O(\log n)$ factor. Moreover, our result can be extended to solve the directed version of the problem introduced by Kannan and Vinay \cite{KV99}. #R##N#Given an integer $\tilde{D} \geq D$ and $\Omega(1/\tilde{D}) < \epsilon < 1/4$, we give an $O(\log^2 n \cdot (\log^{2.71} \Delta) /\epsilon^2)$-round deterministic algorithm in the \textsf{CONGEST} model that computes an orientation where the outdegree of every vertex is upper bounded by $(1+\epsilon)\tilde{D}$. Previously, the best deterministic algorithm for this problem is by Harris \cite{Harris19} that runs in $\tilde{O}((\log^6 n) / \epsilon^4)$ rounds in the \local model.

Includes members:
  Member "near optimal fully dynamic densest subgraph":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flowless extracting densest subgraphs without flow computations")
  Member "improved distributed expander decomposition and nearly optimal triangle enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "distributed approximation algorithms for maximum matching in graphs and hypergraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "improved deterministic distributed matching via rounding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed maximum matching verification in congest")
  Member "improved distributed degree splitting and edge coloring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the complexity of distributed splitting problems")
  Member "on derandomizing local distributed algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "distributed degree splitting edge coloring and orientations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed edge coloring in time quasi polylogarithmic in delta")
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "near optimal fully dynamic densest subgraph")

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)