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: "submodular inference of diffusion networks from multiple trees"
  Abstract: Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.

Connected through hyperedge HE-1 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"

Connected through hyperedge HE-2 (induced by source paper "dynamic network model from partial observations"):
  with members:
    - "topological recurrent neural network for diffusion prediction"
    - "deepcas an end to end predictor of information cascades"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-3 (induced by source paper "dani a fast diffusion aware network inference algorithm"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "virality prediction and community structure in social networks"
    - "a bayesian framework for community detection integrating content and link"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "learning network structures from contagion"):
  with members:
    - "trace complexity of network inference"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-5 (induced by source paper "submodular variational inference for network reconstruction"):
  with members:
    - "polyhedral aspects of submodularity convexity and concavity"
    - "seeing the unseen network inferring hidden social ties from respondent driven sampling"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "fast semidifferential based submodular function optimization"
    - "uncovering the temporal dynamics of diffusion networks"
    - "topology discovery of sparse random graphs with few participants"

Connected through hyperedge HE-6 (induced by source paper "learning and optimization with submodular functions"):
  with members:
    - "active sequential hypothesis testing"
    - "near optimal nonmyopic value of information in graphical models"
    - "uncovering the temporal dynamics of diffusion networks"
    - "learning with submodular functions a convex optimization perspective"
    - "active classification theory and application to underwater inspection"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-7 (induced by source paper "back to the past source identification in diffusion networks from partially observed cascades"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "influence maximization in continuous time diffusion networks"
    - "structure and dynamics of information pathways in online media"
    - "locating the source of diffusion in large scale networks"
    - "four degrees of separation"

Connected through hyperedge HE-8 (induced by source paper "structure and dynamics of information pathways in online media"):
  with members:
    - "influence maximization in continuous time diffusion networks"
    - "finding the graph of epidemic cascades"
    - "information diffusion and external influence in networks"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

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)