human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "eggs a flexible approach to relational modeling of social network spam"):
  Abstract: Social networking websites face a constant barrage of spam, unwanted messages that distract, annoy, and even defraud honest users. These messages tend to be very short, making them difficult to identify in isolation. Furthermore, spammers disguise their messages to look legitimate, tricking users into clicking on links and tricking spam filters into tolerating their malicious behavior. Thus, some spam filters examine relational structure in the domain, such as connections among users and messages, to better identify deceptive content. However, even when it is used, relational structure is often exploited in an incomplete or ad hoc manner. In this paper, we present Extended Group-based Graphical models for Spam (EGGS), a general-purpose method for classifying spam in online social networks. Rather than labeling each message independently, we group related messages together when they have the same author, the same content, or other domain-specific connections. To reason about related messages, we combine two popular methods: stacked graphical learning (SGL) and probabilistic graphical models (PGM). Both methods capture the idea that messages are more likely to be spammy when related messages are also spammy, but they do so in different ways; SGL uses sequential classifier predictions and PGMs use probabilistic inference. We apply our method to four different social network domains. EGGS is more accurate than an independent model in most experimental settings, especially when the correct label is uncertain. For the PGM implementation, we compare Markov logic networks to probabilistic soft logic and find that both work well with neither one dominating, and the combination of SGL and PGMs usually performs better than either on its own.

Includes members:
  Member "detecting smart spammers on social network a topic model approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collective classification of spam campaigners on twitter a hierarchical meta path based approach")
  Member "the libra toolkit for probabilistic models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blankets joint posterior score for learning markov network structures")
  Member "hinge loss markov random fields and probabilistic soft logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational neural machines")
  Member "network analysis of recurring youtube spam campaigns":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language processing sentiment analysis and clinical analytics")

Question: Please tell me which class the source paper 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)