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: "hawkes process for understanding the influence of pathogenic social media accounts"
  Abstract: Over the past years, political events and public opinion on the Web have been allegedly manipulated by accounts dedicated to spreading disinformation and performing malicious activities on social media. These accounts hereafter referred to as "Pathogenic Social Media (PSM)" accounts, are often controlled by terrorist supporters, water armies or fake news writers and hence can pose threats to social media and general public. Understanding and analyzing PSMs could help social media firms devise sophisticated and automated techniques that could be deployed to stop them from reaching their audience and consequently reduce their threat. In this paper, we leverage the well-known statistical technique "Hawkes Process" to quantify the influence of PSM accounts on the dissemination of malicious information on social media platforms. Our findings on a real-world ISIS-related dataset from Twitter indicate that PSMs are significantly different from regular users in making a message viral. Specifically, we observed that PSMs do not usually post URLs from mainstream news sources. Instead, their tweets usually receive large impact on audience, if contained URLs from Facebook and alternative news outlets. In contrary, tweets posted by regular users receive nearly equal impression regardless of the posted URLs and their sources. Our findings can further shed light on understanding and detecting PSM accounts.

Connected through hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts"):
  with members:
    - "privacy aware recommendation with private attribute protection using adversarial learning"
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "signed link prediction with sparse data the role of personality information"
    - "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"
    - "predicting factuality of reporting and bias of news media sources"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"

Connected through hyperedge HE-2 (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"
    - "detection of violent extremists in social media"
    - "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"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "signed link analysis in social media networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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-5 (induced by source paper "signed link prediction with sparse data the role of personality information"):
  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"
    - "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.SI(Social and Information Networks)