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 "approximating large frequency moments with o n 1 2 k bits"):
  Abstract: In this paper we consider the problem of approximating frequency moments in the streaming model. Given a stream $D = \{p_1,p_2,\dots,p_m\}$ of numbers from $\{1,\dots, n\}$, a frequency of $i$ is defined as $f_i = |\{j: p_j = i\}|$. The $k$-th \emph{frequency moment} of $D$ is defined as $F_k = \sum_{i=1}^n f_i^k$. #R##N#In this paper we give an upper bound on the space required to find a $k$-th frequency moment of $O(n^{1-2/k})$ bits that matches, up to a constant factor, the lower bound of Woodruff and Zhang (STOC 12) for constant $\epsilon$ and constant $k$. Our algorithm makes a single pass over the stream and works for any constant $k > 3$.

Includes members:
  Member "a lower bound for estimating high moments of a data stream":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on approximating functions of the singular values in a stream")
  Member "polynomial estimators for high frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal sketches for the frequency negative moments and other decreasing streaming sums")
  Member "tight bounds for distributed functional monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards optimal moment estimation in streaming and distributed models")
  Member "streaming algorithms from precision sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation")
  Member "recursive sketching for frequency moments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "asymptotically optimal lower bounds on the nih multi party information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards optimal moment estimation in streaming and distributed models")

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)