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: "tracking influential individuals in dynamic networks"
  Abstract: In this paper, we tackle a challenging problem inherent in a series of applications: tracking the influential nodes in dynamic networks. Specifically, we model a dynamic network as a stream of edge weight updates. This general model embraces many practical scenarios as special cases, such as edge and node insertions, deletions as well as evolving weighted graphs. Under the popularly adopted linear threshold model and independent cascade model, we consider two essential versions of the problem: finding the nodes whose influences passing a user specified threshold and finding the top-  $k$       most influential nodes. Our key idea is to use the polling-based methods and maintain a sample of random RR sets so that we can approximate the influence of nodes with provable quality guarantees. We develop an efficient algorithm that incrementally updates the sample random RR sets against network changes. We also design methods to determine the proper sample sizes for the two versions of the problem so that we can provide strong quality guarantees and, at the same time, be efficient in both space and time. In addition to the thorough theoretical results, our experimental results on five real network data sets clearly demonstrate the effectiveness and efficiency of our algorithms.

Connected through hyperedge HE-1 (induced by source paper "tracking influential nodes in time decaying dynamic interaction networks"):
  with members:
    - "real time influence maximization on dynamic social streams"
    - "submodular optimization over sliding windows"
    - "robust influence maximization"
    - "boosting information spread an algorithmic approach"
    - "submodular maximization over sliding windows"
    - "online influence maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-2 (induced by source paper "limiting the spread of fake news on social media platforms by evaluating users trustworthiness"):
  with members:
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "computational fact checking from knowledge networks"
    - "events and controversies influences of a shocking news event on information seeking"
    - "tweetcred real time credibility assessment of content on twitter"
    - "processing social media messages in mass emergency a survey"
    - "using a model of social dynamics to predict popularity of news"

Connected through hyperedge HE-3 (induced by source paper "technical report accelerating dynamic graph analytics on gpus"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "approximate personalized pagerank on dynamic graphs"
    - "gunrock a high performance graph processing library on the gpu"
    - "graph based anomaly detection and description a survey"
    - "fast sparse matrix vector multiplication on gpus implications for graph mining"

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)