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 "approximate personalized pagerank on dynamic graphs"):
  Abstract: We propose and analyze two algorithms for maintaining approximate Personalized PageRank (PPR) vectors on a dynamic graph, where edges are added or deleted. Our algorithms are natural dynamic versions of two known local variations of power iteration. One, Forward Push, propagates probability mass forwards along edges from a source node, while the other, Reverse Push, propagates local changes backwards along edges from a target. In both variations, we maintain an invariant between two vectors, and when an edge is updated, our algorithm first modifies the vectors to restore the invariant, then performs any needed local push operations to restore accuracy. #R##N#For Reverse Push, we prove that for an arbitrary directed graph in a random edge model, or for an arbitrary undirected graph, given a uniformly random target node $t$, the cost to maintain a PPR vector to $t$ of additive error $\varepsilon$ as $k$ edges are updated is $O(k + \bar{d} / \varepsilon)$, where $\bar{d}$ is the average degree of the graph. This is $O(1)$ work per update, plus the cost of computing a reverse vector once on a static graph. For Forward Push, we show that on an arbitrary undirected graph, given a uniformly random start node $s$, the cost to maintain a PPR vector from $s$ of degree-normalized error $\varepsilon$ as $k$ edges are updated is $O(k + 1 / \varepsilon)$, which is again $O(1)$ per update plus the cost of computing a PPR vector once on a static graph.

Includes members:
  Member "personalized pagerank estimation and search a bidirectional approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs")
  Member "frogwild fast pagerank approximations on graph engines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hogwild gibbs can be panaccurate")
  Member "efficient algorithms for personalized pagerank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries")
  Member "bidirectional pagerank estimation from average case to worst case":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep node ranking structural network embedding and end to end node classification")
  Member "fast ppr scaling personalized pagerank estimation for large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient algorithms for approximate single source personalized pagerank queries")
  Member "on the complexity of the monte carlo method for incremental pagerank":
    also connected through other hyperedges:
      (none)
  Member "pagerank beyond the web":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonlinearity networks a 2020 vision")
  Member "defining and evaluating network communities based on ground truth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)