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: "personalized pagerank estimation and search a bidirectional approach"
  Abstract: We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators in experiments on a diverse set of networks. Moreover, it has a clean algebraic structure which enables it to be used as a primitive for the Personalized PageRank Search problem: Given a network like Facebook, a query like "people named John," and a searching user, return the top nodes in the network ranked by PPR from the perspective of the searching user. Previous solutions either score all nodes or score candidate nodes one at a time, which is prohibitively slow for large candidate sets. We develop a new algorithm based on our bidirectional PPR estimator which identifies the most relevant results by sampling candidates based on their PPR; this is the first solution to PPR search that can find the best results without iterating through the set of all candidate results. Finally, by combining PPR sampling with sequential PPR estimation and Monte Carlo, we develop practical algorithms for PPR search, and we show via experiments that our algorithms are efficient on networks with billions of edges.

Connected through hyperedge HE-1 (induced by source paper "empirical policy evaluation with supergraphs"):
  with members:
    - "forward backward reinforcement learning"
    - "recall traces backtracking models for efficient reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"
    - "on the role of clustering in personalized pagerank estimation"
    - "fast bidirectional probability estimation in markov models"
    - "personalized pagerank to a target node"

Connected through hyperedge HE-2 (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"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-3 (induced by source paper "scalable graph embeddings via sparse transpose proximities"):
  with members:
    - "fast randomized pca for sparse data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "fast warped graph embedding unifying framework and one click algorithm"

Connected through hyperedge HE-4 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"

Connected through hyperedge HE-5 (induced by source paper "analysis of evolutionary behavior in self learning media search engines"):
  with members:
    - "triplet based deep hashing network for cross modal retrieval"
    - "self supervised video hashing with hierarchical binary auto encoder"
    - "a joint sequence fusion model for video question answering and retrieval"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "optimizing query evaluations using reinforcement learning for web search"
    - "sketch based manga retrieval using manga109 dataset"
    - "cross media similarity evaluation for web image retrieval in the wild"

Connected through hyperedge HE-6 (induced by source paper "prsim sublinear time simrank computation on large power law graphs"):
  with members:
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "sling a near optimal index structure for simrank"
    - "approximate personalized pagerank on dynamic graphs"
    - "panther fast top k similarity search in large networks"
    - "algorithmic complexity of power law networks"
    - "efficient simrank computation via linearization"
    - "axiomatic ranking of network role similarity"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-7 (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"
    - "approximate personalized pagerank on dynamic graphs"
    - "fast distributed pagerank computation"
    - "efficient algorithms for personalized pagerank"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-8 (induced by source paper "pairwise link prediction"):
  with members:
    - "the hyperkron graph model for higher order features"
    - "simplicial closure and higher order link prediction"
    - "the spacey random walk a stochastic process for higher order data"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "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)