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: "bidirectional pagerank estimation from average case to worst case"
  Abstract: We present a new algorithm for estimating the Personalized PageRank (PPR) between a source and target node on undirected graphs, with sublinear running-time guarantees over the worst-case choice of source and target nodes. Our work builds on a recent line of work on bidirectional estimators for PPR, which obtained sublinear running-time guarantees but in an average-case sense, for a uniformly random choice of target node. Crucially, we show how the reversibility of random walks on undirected networks can be exploited to convert average-case to worst-case guarantees. While past bidirectional methods combine forward random walks with reverse local pushes, our algorithm combines forward local pushes with reverse random walks. We also discuss how to modify our methods to estimate random-walk probabilities for any length distribution, thereby obtaining fast algorithms for estimating general graph diffusions, including the heat kernel, on undirected networks.

Connected through hyperedge HE-1 (induced by source paper "deep node ranking structural network embedding and end to end node classification"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "graph embedding techniques applications and performance a survey"
    - "attention is all you need"
    - "predicting multicellular function through multi layer tissue networks"
    - "a unified approach to interpreting model predictions"
    - "a survey on network embedding"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "virtual web based personalized pagerank updating"):
  with members:
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "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-3 (induced by source paper "on approximating the stationary distribution of time reversible markov chains"):
  with members:
    - "fast bidirectional probability estimation in markov models"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "solving systems of linear equations locally and asynchronously"
    - "sublinear algorithms for local graph centrality estimation"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-4 (induced by source paper "approximate personalized pagerank on dynamic graphs"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "frogwild fast pagerank approximations on graph engines"
    - "efficient algorithms for personalized pagerank"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "on the complexity of the monte carlo method for incremental pagerank"
    - "pagerank beyond the web"
    - "defining and evaluating network communities based on ground truth"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-5 (induced by source paper "efficient algorithms for personalized pagerank"):
  with members:
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a note on the pagerank of undirected graphs"
    - "fast bidirectional probability estimation in markov models"
    - "fast distributed pagerank computation"
    - "learning fast mixing models for structured prediction"
    - "heat kernel based community detection"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "computing the stationary distribution locally"

Connected through hyperedge HE-6 (induced by source paper "localization in seeded pagerank"):
  with members:
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "a scalable generative graph model with community structure"
    - "pagerank beyond the web"
    - "sublinear algorithms for local graph centrality estimation"
    - "fast matrix computations for pairwise and columnwise commute times and katz scores"
    - "quick detection of nodes with large degrees"
    - "a sequential algorithm for generating random graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "sublinear algorithms for local graph centrality estimation"):
  with members:
    - "on approximating the stationary distribution of time reversible markov chains"
    - "an optimization approach to locally biased graph algorithms"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "the power of local information in pagerank"
    - "simple set cardinality estimation through random sampling"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"

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)