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: "followers or phantoms an anatomy of purchased twitter followers"
  Abstract: Online Social Media (OSM) is extensively used by contemporary Internet users to communicate, socialize and disseminate information. This has led to the creation of a distinct online social identity which in turn has created the need of online social reputation management techniques. A significant percentage of OSM users utilize various methods to drive and manage their reputation on OSM. This has given rise to underground markets which buy/sell fraudulent accounts, `likes', `comments' (Facebook, Instagram) and `followers' (Twitter) to artificially boost their social reputation. In this study, we present an anatomy of purchased followers on Twitter and their behaviour. We illustrate in detail the profile characteristics, content sharing and behavioural patterns of purchased follower accounts. #R##N#Previous studies have analyzed the purchased follower markets and customers. Ours is the first study which analyzes the anatomy of purchased followers accounts. Some of the key insights of our study show that purchased followers have a very high unfollow entropy rate and low social engagement with their friends. In addition, we noticed that purchased follower accounts have significant difference in their interaction and content sharing patterns in comparison to random Twitter users. We also found that underground markets do not follow their service policies and guarantees they provide to customer. Our study highlights the key identifiers for suspicious follow behaviour. We then built a supervised learning mechanism to predict suspicious follower behaviour with 88.2% accuracy. We believe that understanding the anatomy and characteristics of purchased followers can help detect suspicious follower behaviour and fraudulent accounts to a larger extent.

Connected through hyperedge HE-1 (induced by source paper "sample nlpde and nlode social media modeling of information transmission for infectious diseases case study ebola"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "a computer virus propagation model using delay differential equations with probabilistic contagion and immunity"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "statistically significant detection of linguistic change"
    - "do retweets indicate interest trust agreement extended abstract"
    - "a cooperate defect model for the spread of deviant behavior in social networks"
    - "what they do in shadows twitter underground follower market"

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)