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 "streaming frequent items with timestamps and detecting large neighborhoods in graph streams"):
  Abstract: Detecting frequent items is a fundamental problem in data streaming research. However, in many applications, besides the frequent items themselves, meta data such as the timestamps of when the frequent items appeared or other application-specific data that "arrives" with the frequent items needs to be reported too. #R##N#To this end, we introduce the Neighborhood Detection problem in graph streams, which both accurately models situations such as those stated above, and addresses the fundamental problem of detecting large neighborhoods or stars in graph streams. In Neighborhood Detection, an algorithm receives the edges of a bipartite graph $G=(A, B, E)$ with $|A| = n$ and $|B| = \text{poly}~n$ in arbitrary order and is given a threshold parameter $d$. Provided that there is at least one $A$-node of degree at least $d$, the objective is to output a node $a \in A$ together with at least $\frac{d}{c}$ of its neighbors, where $c$ is the approximation factor. #R##N#We show that in insertion-only streams, there is a one-pass $\tilde{O}(n + n^{\frac{1}{c}}d)$ space $c$-approximation streaming algorithm, for integral values of $c \ge 2$. We complement this result with a lower bound, showing that computing a $(c/1.01)$-approximation requires space $\Omega(n / c^2 + n^{\frac{1}{c-1}}d / c^2)$, for any integral $c \ge 2$, which renders our algorithm optimal for a large range of settings (up to logarithmic factors). In insertion-deletion (turnstile) streams, we give a one-pass $c$-approximation algorithm with space $\tilde{O}(\frac{dn}{c^2})$ (if $c \le \sqrt{n}$). We also prove that this is best possible up to logarithmic factors. #R##N#Our lower bounds are obtained by defining new multi-party and two-party communication problems, respectively, and proving lower bounds on their communication complexities using information theoretic arguments.

Includes members:
  Member "optimality of linear sketching under modular updates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate mathbb f _2 sketching of valuation functions")
  Member "independent sets in vertex arrival streams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "tight space approximation tradeoff for the multi pass streaming set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "tight bounds for single pass streaming complexity of the set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "towards tight bounds for the streaming set cover problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "maximum matching in turnstile streams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "semi streaming set cover":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "tight bounds for lp samplers finding duplicates in streams and related problems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")

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)