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: "scalable influence estimation in continuous time diffusion networks"
  Abstract: If a piece of information is released from a media site, can it spread, in 1 month, to a million web pages? This influence estimation problem is very challenging since both the time-sensitive nature of the problem and the issue of scalability need to be addressed simultaneously. In this paper, we propose a randomized algorithm for influence estimation in continuous-time diffusion networks. Our algorithm can estimate the influence of every node in a network with |V| nodes and |E| edges to an accuracy of $\varepsilon$ using $n=O(1/\varepsilon^2)$ randomizations and up to logarithmic factors O(n|E|+n|V|) computations. When used as a subroutine in a greedy influence maximization algorithm, our proposed method is guaranteed to find a set of nodes with an influence of at least (1-1/e)OPT-2$\varepsilon$, where OPT is the optimal value. Experiments on both synthetic and real-world data show that the proposed method can easily scale up to networks of millions of nodes while significantly improves over previous state-of-the-arts in terms of the accuracy of the estimated influence and the quality of the selected nodes in maximizing the influence.

Connected through hyperedge HE-1 (induced by source paper "approximate k cover in hypergraphs efficient algorithms and applications"):
  with members:
    - "outward influence and cascade size estimation in billion scale networks"
    - "scalable betweenness centrality maximization via sampling"
    - "better streaming algorithms for the maximum coverage problem"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-2 (induced by source paper "parallelism in randomized incremental algorithms"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel shortest paths using radius stepping"
    - "efficient construction of probabilistic tree embeddings"
    - "parallel correlation clustering on big graphs"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-3 (induced by source paper "robust budget allocation via continuous submodular functions"):
  with members:
    - "robust monotone submodular function maximization"
    - "submodular optimization under noise"
    - "subquadratic submodular function minimization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "robust influence maximization"
    - "robust influence maximization"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "submodular functions from discrete to continous domains"

Connected through hyperedge HE-4 (induced by source paper "bootstrapped graph diffusions exposing the power of nonlinearity"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "semi supervised learning on graphs through reach and distance diffusion"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "deep convolutional networks on graph structured data"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "distilling information reliability and source trustworthiness from digital traces"):
  with members:
    - "uncovering the dynamics of crowdlearning and the value of knowledge"
    - "multistage campaigning in social networks"
    - "assessing human error against a benchmark of perfection"
    - "smart broadcasting do you want to be seen"
    - "from data fusion to knowledge fusion"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "knowledge based trust estimating the trustworthiness of web sources"

Connected through hyperedge HE-6 (induced by source paper "outward influence and cascade size estimation in billion scale networks"):
  with members:
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "multiple infection sources identification with provable guarantees"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion"):
  with members:
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "reverse ranking by graph structure model and scalable algorithms"
    - "large scale distributed semi supervised learning using streaming approximation"
    - "deepwalk online learning of social representations"
    - "fast ppr scaling personalized pagerank estimation for large graphs"

Connected through hyperedge HE-8 (induced by source paper "robust influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization"
    - "stability of influence maximization"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "trace complexity of network inference"
    - "analytical approach to parallel repetition"
    - "maximizing social influence in nearly optimal time"

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.SI(Social and Information Networks)