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: "identifying community structures in dynamic networks"
  Abstract: Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

Connected through hyperedge HE-1 (induced by source paper "hawkes process for understanding the influence of pathogenic social media accounts"):
  with members:
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "the spread of low credibility content by social bots"
    - "semi supervised learning for detecting human trafficking"
    - "the spread of fake news by social bots"

Connected through hyperedge HE-2 (induced by source paper "privacy in social media identification mitigation and applications"):
  with members:
    - "social media and user privacy"
    - "similar but different exploiting users congruity for recommendation systems"
    - "a survey of location prediction on twitter"
    - "securing social media user data an adversarial approach"
    - "blind de anonymization attacks using social networks"
    - "walk2friends inferring social links from mobility profiles"
    - "on content based recommendation and user privacy in social tagging systems"
    - "you are who you know and how you behave attribute inference attacks via users social friends and behaviors"

Connected through hyperedge HE-3 (induced by source paper "early identification of pathogenic social media accounts"):
  with members:
    - "detecting pathogenic social media accounts without content or network structure"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "securing social media user data an adversarial approach"
    - "deep learning for hate speech detection in tweets"
    - "semi supervised learning for detecting human trafficking"
    - "the spread of fake news by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-4 (induced by source paper "social media and user privacy"):
  with members:
    - "similar but different exploiting users congruity for recommendation systems"
    - "securing social media user data an adversarial approach"
    - "de anonymizing social networks"

Connected through hyperedge HE-5 (induced by source paper "causal inference for early detection of pathogenic social media accounts"):
  with members:
    - "similar but different exploiting users congruity for recommendation systems"
    - "securing social media user data an adversarial approach"
    - "the temporal logic of causal structures"

Connected through hyperedge HE-6 (induced by source paper "similar but different exploiting users congruity for recommendation systems"):
  with members:
    - "signed link analysis in social media networks"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning for detecting human trafficking"):
  with members:
    - "a non parametric learning approach to identify online human trafficking"
    - "signed link analysis in social media networks"
    - "an entity resolution approach to isolate instances of human trafficking online"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-8 (induced by source paper "twitter hashtag recommendation using matrix factorization"):
  with members:
    - "signed link analysis in social media networks"

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)