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: "sketch based influence maximization and computation scaling up with guarantees"
  Abstract: Propagation of contagion through networks is a fundamental process. It is used to model the spread of information, influence, or a viral infection. Diffusion patterns can be specified by a probabilistic model, such as Independent Cascade (IC), or captured by a set of representative traces.   Basic computational problems in the study of diffusion are influence queries (determining the potency of a specified seed set of nodes) and Influence Maximization (identifying the most influential seed set of a given size). Answering each influence query involves many edge traversals, and does not scale when there are many queries on very large graphs. The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence. Greedy has a guaranteed approximation ratio of at least (1-1/e) and actually produces a sequence of nodes, with each prefix having approximation guarantee with respect to the same-size optimum. Since Greedy does not scale well beyond a few million edges, for larger inputs one must currently use either heuristics or alternative algorithms designed for a pre-specified small seed set size.   We develop a novel sketch-based design for influence computation. Our greedy Sketch-based Influence Maximization (SKIM) algorithm scales to graphs with billions of edges, with one to two orders of magnitude speedup over the best greedy methods. It still has a guaranteed approximation ratio, and in practice its quality nearly matches that of exact greedy. We also present influence oracles, which use linear-time preprocessing to generate a small sketch for each node, allowing the influence of any seed set to be quickly answered from the sketches of its nodes.

Connected through hyperedge HE-1 (induced by source paper "rcelf a residual based approach for influence maximization problem"):
  with members:
    - "importance sketching of influence dynamics in billion scale networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "imrank influence maximization via finding self consistent ranking"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "staticgreedy solving the scalability accuracy dilemma in influence maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "seeding with costly network information"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "metric sublinear algorithms via linear sampling"
    - "the importance of communities for learning to influence"
    - "stochastic submodular maximization the case of coverage functions"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "robust influence maximization"
    - "almost optimal streaming algorithms for coverage problems"
    - "online influence maximization"

Connected through hyperedge HE-3 (induced by source paper "efficient approximation algorithms for adaptive seed minimization"):
  with members:
    - "cost effective seed selection in online social networks"
    - "influence maximization meets efficiency and effectiveness a hop based approach"
    - "adaptive influence maximization in social networks why commit when you can adapt"
    - "holistic influence maximization combining scalability and efficiency with opinion aware models"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "scalable methods for adaptively seeding a social network"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"
    - "imrank influence maximization via finding self consistent ranking"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence for low resource communities influence maximization in an uncertain world"):
  with members:
    - "learning in pomdps with monte carlo tree search"
    - "activating the breakfast club modeling influence spread in natural world social networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "maximizing social influence in nearly optimal time"
    - "community structure and scale free collections of erdős renyi graphs"
    - "information theoretic measures of influence based on content dynamics"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "scalable influence estimation without sampling"):
  with members:
    - "cascade size distributions and why they matter"
    - "outward influence and cascade size estimation in billion scale networks"
    - "nonbacktracking bounds on the influence in independent cascade models"
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "robust influence maximization"
    - "reconstructing parameters of spreading models from partial observations"
    - "robust influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-6 (induced by source paper "submodular cost submodular cover with an approximate oracle"):
  with members:
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "cost effective seed selection in online social networks"
    - "why approximate when you can get the exact optimal targeted viral marketing at scale"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "scalable bicriteria algorithms for the threshold activation problem in online social networks"
    - "submodular optimization under noise"
    - "an efficient streaming algorithm for the submodular cover problem"
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"

Connected through hyperedge HE-7 (induced by source paper "maximizing welfare in social networks under a utility driven influence diffusion model"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "an issue in the martingale analysis of the influence maximization algorithm imm"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "inferring networks of substitutable and complementary products"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-8 (induced by source paper "evolving influence maximization in evolving networks"):
  with members:
    - "adaptive influence maximization in dynamic social networks"
    - "time is what prevents everything from happening at once propagation time conscious influence maximization"
    - "outward influence and cascade size estimation in billion scale networks"
    - "online influence maximization in non stationary social networks"
    - "boosting information spread an algorithmic approach"
    - "online influence maximization"
    - "seed selection for spread of influence in social networks temporal vs static approach"
    - "influence maximization near optimal time complexity meets practical efficiency"

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)