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 "fast ppr scaling personalized pagerank estimation for large graphs"):
  Abstract: We propose a new algorithm, FAST-PPR, for estimating personalized PageRank: given start node $s$ and target node $t$ in a directed graph, and given a threshold $\delta$, FAST-PPR estimates the Personalized PageRank $\pi_s(t)$ from $s$ to $t$, guaranteeing a small relative error as long $\pi_s(t)>\delta$. Existing algorithms for this problem have a running-time of $\Omega(1/\delta)$; in comparison, FAST-PPR has a provable average running-time guarantee of ${O}(\sqrt{d/\delta})$ (where $d$ is the average in-degree of the graph). This is a significant improvement, since $\delta$ is often $O(1/n)$ (where $n$ is the number of nodes) for applications. We also complement the algorithm with an $\Omega(1/\sqrt{\delta})$ lower bound for PageRank estimation, showing that the dependence on $\delta$ cannot be improved. #R##N#We perform a detailed empirical study on numerous massive graphs, showing that FAST-PPR dramatically outperforms existing algorithms. For example, on the 2010 Twitter graph with 1.5 billion edges, for target nodes sampled by popularity, FAST-PPR has a $20$ factor speedup over the state of the art. Furthermore, an enhanced version of FAST-PPR has a $160$ factor speedup on the Twitter graph, and is at least $20$ times faster on all our candidate graphs.

Includes members:
  Member "fast distributed pagerank computation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "walking randomly massively and efficiently")
  Member "personalized pagerank to a target node":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")
  Member "fast incremental and personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "supervised random walks predicting and recommending links in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")
  Member "layered label propagation a multiresolution coordinate free ordering for compressing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "2ps high quality edge partitioning with two phase streaming")

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)