human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries"):
  with members:
    - "distributed algorithms for fully personalized pagerank on large graphs"
    - "prsim sublinear time simrank computation on large power law graphs"
    - "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "fast distributed pagerank computation"
    - "efficient algorithms for personalized pagerank"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-2 (induced by source paper "distributed algorithms for fully personalized pagerank on large graphs"):
  with members:
    - "pixie a system for recommending 3 billion items to 200 million users in real time"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "powerwalk scalable personalized pagerank via random walks with vertex centric decomposition"
    - "fast distributed pagerank computation"

Connected through hyperedge HE-3 (induced by source paper "virtual web based personalized pagerank updating"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "pagerank beyond the web"
    - "fast incremental and personalized pagerank"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-4 (induced by source paper "predict then propagate graph neural networks meet personalized pagerank"):
  with members:
    - "mean field theory of graph neural networks in graph partitioning"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "damping effect on pagerank distribution"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning with competitive infection models"):
  with members:
    - "semi supervised learning on graphs through reach and distance diffusion"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "active semi supervised learning using sampling theory for graph signals"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "distributed representations of words and phrases and their compositionality"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-7 (induced by source paper "on approximating the stationary distribution of time reversible markov chains"):
  with members:
    - "fast bidirectional probability estimation in markov models"
    - "bidirectional pagerank estimation from average case to worst case"
    - "solving systems of linear equations locally and asynchronously"
    - "sublinear algorithms for local graph centrality estimation"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-8 (induced by source paper "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs"):
  with members:
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "fast and exact top k search for random walk with restart"
    - "supervised random walks predicting and recommending links in social networks"

Question: Please tell me which class the center node 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)