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: "signed link analysis in social media networks"
  Abstract: Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially distinct from those of unsigned networks, thus we need dedicated efforts on link analysis in signed social networks. In this paper, following social theories in link analysis in unsigned networks, we adopt three social science theories, namely Emotional Information, Diffusion of Innovations and Individual Personality, to guide the task of link analysis in signed networks.

Connected through hyperedge HE-1 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "privacy in social media identification mitigation and applications"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "signed network modeling based on structural balance theory"
    - "semi supervised learning for detecting human trafficking"

Connected through hyperedge HE-2 (induced by source paper "signed link prediction with sparse data the role of personality information"):
  with members:
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "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"
    - "semi supervised learning for detecting human trafficking"
    - "negative link prediction in social media"

Connected through hyperedge HE-3 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "a survey of signed network mining in social media"
    - "controversy and sentiment in online news"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "the slashdot zoo mining a social network with negative edges"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-4 (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"
    - "identifying community structures in dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "investigating negative interactions in multiplex networks a mutual information approach"):
  with members:
    - "leveraging network dynamics for improved link prediction"
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "link prediction in social networks the state of the art"
    - "link prediction in complex networks a mutual information perspective"
    - "applying weighted pagerank to author citation networks"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-6 (induced by source paper "securing social media user data an adversarial approach"):
  with members:
    - "similar but different exploiting users congruity for recommendation systems"
    - "link prediction by de anonymization how we won the kaggle social network challenge"
    - "de anonymizing social networks"

Connected through hyperedge HE-7 (induced by source paper "detecting antagonistic and allied communities on social media"):
  with members:
    - "sentiment driven community profiling and detection on social media"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"

Connected through hyperedge HE-8 (induced by source paper "similar but different exploiting users congruity for recommendation systems"):
  with members:
    - "identifying community structures in dynamic 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)