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 "efficient algorithms for approximate single source personalized pagerank queries"):
  Abstract: Given a graph $G$, a source node $s$ and a target node $t$, the personalized PageRank (PPR) of $t$ with respect to $s$ is the probability that a random walk starting from $s$ terminates at $t$. An important variant of the PPR query is single-source PPR (SSPPR), which enumerates all nodes in $G$, and returns the top-$k$ nodes with the highest PPR values with respect to a given source $s$. PPR in general and SSPPR in particular have important applications in web search and social networks, e.g., in Twitter's Who-To-Follow recommendation service. However, PPR computation is known to be expensive on large graphs, and resistant to indexing. Consequently, previous solutions either use heuristics, which do not guarantee result quality, or rely on the strong computing power of modern data centers, which is costly. #R##N#Motivated by this, we propose effective index-free and index-based algorithms for approximate PPR processing, with rigorous guarantees on result quality. We first present FORA, an approximate SSPPR solution that combines two existing methods Forward Push (which is fast but does not guarantee quality) and Monte Carlo Random Walk (accurate but slow) in a simple and yet non-trivial way, leading to both high accuracy and efficiency. Further, FORA includes a simple and effective indexing scheme, as well as a module for top-$k$ selection with high pruning power. Extensive experiments demonstrate that the proposed solutions are orders of magnitude more efficient than their respective competitors. Notably, on a billion-edge Twitter dataset, FORA answers a top-500 approximate SSPPR query within 1 second, using a single commodity server.

Includes members:
  Member "distributed algorithms for fully personalized pagerank on large graphs":
    also connected through other hyperedges:
      (none)
  Member "prsim sublinear time simrank computation on large power law graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "realtime index free single source simrank processing on web scale graphs")
  Member "tpa fast scalable and accurate method for approximate random walk with restart on billion scale graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast and accurate random walk with restart on dynamic graphs with guarantees")
  Member "personalized pagerank estimation and search a bidirectional approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "approximate personalized pagerank on dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems")
  Member "fast distributed pagerank computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "walking randomly massively and efficiently")
  Member "efficient algorithms for personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate personalized pagerank on dynamic graphs")
  Member "fast ppr scaling personalized pagerank estimation for large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed algorithms for fully personalized pagerank on large graphs")

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.SI(Social and Information Networks)