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: "timed influence computation and maximization"
  Abstract: We consider a cost model for diffusion in a network that captures both the scope of infection and its propagation time: The edges of the network have associated lengths which model transmission times, and influence scores are higher for faster propagation. We propose an intuitive measure of timed influence, which extends and unifies several classic measures, including the well-studied “binary” influence [Richardson and Domingos 2002; Kempe et al. 2003] (which only measures scope), a recently-studied threshold model of timed influence [Gomez-Rodriguez et al. 2011] (which considers a node influenced only within a fixed time horizon), and closeness centrality (which is extended from being defined for a single node to multiple seed nodes and from a fixed network to distributions). Finally, we provide the first highly scalable algorithms for timed influence computation and maximization. In particular, we improve by orders of magnitude the scalability of state-of-the-art threshold timed influence computation. Moreover, our design provides robust guarantees and is novel also as a theoretical contribution.

Connected through hyperedge HE-1 (induced by source paper "reverse ranking by graph structure model and scalable algorithms"):
  with members:
    - "scalable facility location for massive graphs on pregel like systems"
    - "multi objective weighted sampling"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "all distances sketches revisited hip estimators for massive graphs analysis"
    - "trace complexity of network inference"
    - "scalable influence estimation in continuous time diffusion networks"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-2 (induced by source paper "influence prediction for continuous time information propagation on networks"):
  with members:
    - "modeling information propagation with survival theory"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-3 (induced by source paper "all distances sketches revisited hip estimators for massive graphs analysis"):
  with members:
    - "computing classic closeness centrality at scale"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "axioms for centrality"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "four degrees of separation"
    - "robustness of social networks comparative results based on distance distributions"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"
    - "fast approximation of centrality"

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)