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: "privacy in social media identification mitigation and applications"
  Abstract: The increasing popularity of social media has attracted a huge number of people to participate in numerous activities on a daily basis. This results in tremendous amounts of rich user-generated data. This data provides opportunities for researchers and service providers to study and better understand users' behaviors and further improve the quality of the personalized services. Publishing user-generated data risks exposing individuals' privacy. Users privacy in social media is an emerging task and has attracted increasing attention in recent years. These works study privacy issues in social media from the two different points of views: identification of vulnerabilities, and mitigation of privacy risks. Recent research has shown the vulnerability of user-generated data against the two general types of attacks, identity disclosure and attribute disclosure. These privacy issues mandate social media data publishers to protect users' privacy by sanitizing user-generated data before publishing it. Consequently, various protection techniques have been proposed to anonymize user-generated social media data. There is a vast literature on privacy of users in social media from many perspectives. In this survey, we review the key achievements of user privacy in social media. In particular, we review and compare the state-of-the-art algorithms in terms of the privacy leakage attacks and anonymization algorithms. We overview the privacy risks from different aspects of social media and categorize the relevant works into five groups 1) graph data anonymization and de-anonymization, 2) author identification, 3) profile attribute disclosure, 4) user location and privacy, and 5) recommender systems and privacy issues. We also discuss open problems and future research directions for user privacy issues in social media.

Connected through hyperedge HE-1 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "i am not what i write privacy preserving text representation learning"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "detection of violent extremists in social media"
    - "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"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "signed link analysis in social media networks"

Connected through hyperedge HE-3 (induced by source paper "target privacy preserving for social networks"):
  with members:
    - "multi round influence maximization"
    - "contaminant removal for android malware detection systems"
    - "preserving link privacy in social network based systems"
    - "a model of internet topology using k shell decomposition"
    - "systematic topology analysis and generation using degree correlations"

Connected through hyperedge HE-4 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media"):
  with members:
    - "an end to end framework to identify pathogenic social media accounts on twitter"
    - "detection of violent extremists in social media"
    - "detecting pathogenic social media accounts without content or network structure"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "deep neural networks for bot detection"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"

Connected through hyperedge HE-5 (induced by source paper "hawkes process for understanding the influence of pathogenic social media accounts"):
  with members:
    - "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"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-6 (induced by source paper "i am not what i write privacy preserving text representation learning"):
  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"
    - "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"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-7 (induced by source paper "towards plausible graph anonymization"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "language in our time an empirical analysis of hashtags"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "walk2friends inferring social links from mobility profiles"
    - "evolution of ego networks in social media with link recommendations"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-8 (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"
    - "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"
    - "signed link analysis in social media networks"
    - "negative link prediction in social media"

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.CR(Cryptography and Security)