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 "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"):
  Abstract: While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of {\em both} time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called {\em densest subgraph problem}. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge. #R##N#In a graph $G = (V, E)$, the "density" of a subgraph induced by a subset of nodes $S \subseteq V$ is defined as $|E(S)|/|S|$, where $E(S)$ is the set of edges in $E$ with both endpoints in $S$. In the densest subgraph problem, the goal is to find a subset of nodes that maximizes the density of the corresponding induced subgraph. For any $\epsilon>0$, we present a dynamic algorithm that, with high probability, maintains a $(4+\epsilon)$-approximation to the densest subgraph problem under a sequence of edge insertions and deletions in a graph with $n$ nodes. It uses $\tilde O(n)$ space, and has an amortized update time of $\tilde O(1)$ and a query time of $\tilde O(1)$. Here, $\tilde O$ hides a $O(\poly\log_{1+\epsilon} n)$ term. The approximation ratio can be improved to $(2+\epsilon)$ at the cost of increasing the query time to $\tilde O(n)$. It can be extended to a $(2+\epsilon)$-approximation sublinear-time algorithm and a distributed-streaming algorithm. Our algorithm is the first streaming algorithm that can maintain the densest subgraph in {\em one pass}. The previously best algorithm in this setting required $O(\log n)$ passes [Bahmani, Kumar and Vassilvitskii, VLDB'12]. The space required by our algorithm is tight up to a polylogarithmic factor.

Includes members:
  Member "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "densest subgraph in dynamic graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "applications of uniform sampling densest subgraph and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed dense subgraph detection and low outdegree orientation")
  Member "single pass spectral sparsification in dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "dense subgraph maintenance under streaming edge weight updates for real time story identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining maximal span cores from temporal networks")
  Member "densest subgraph in streaming and mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "tight bounds for lp samplers finding duplicates in streams and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "perfect matchings in o n 1 5 time in regular bipartite graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry")

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)