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: "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
  Abstract: Spambot detection in online social networks is a long-lasting challenge involving the study and design of detection techniques capable of efficiently identifying ever-evolving spammers. Recently, a new wave of  social spambots   has emerged, with advanced human-like characteristics that allow them to go undetected even by current state-of-the-art algorithms. In this paper, we show that efficient spambots detection can be achieved via an in-depth analysis of their collective behaviors exploiting the  digital DNA  technique for modeling the behaviors of social network users. Inspired by its biological counterpart, in the digital DNA representation the behavioral lifetime of a digital account is encoded in a sequence of characters. Then, we define a similarity measure for such digital DNA sequences. We build upon digital DNA and the similarity between groups of users to characterize both genuine accounts and spambots. Leveraging such a characterization, we design the  Social Fingerprinting   technique, which is able to discriminate among spambots and genuine accounts in both a supervised and an unsupervised fashion. We also evaluate the effectiveness of Social Fingerprinting and we compare it with three state-of-the-art detection showing the superiority of our solution. Finally, among the peculiarities of our approach is the possibility to apply off-the-shelf DNA analysis techniques to study online users behaviors and to efficiently rely on a limited number of lightweight account characteristics.

Connected through hyperedge HE-1 (induced by source paper "charting the landscape of online cryptocurrency manipulation"):
  with members:
    - "data mining for detecting bitcoin ponzi schemes"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "polyglot distributed word representations for multilingual nlp"

Connected through hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends"):
  with members:
    - "homogeneity based transmissive process to model true and false news in social networks"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the rise of guardians fact checking url recommendation to combat fake news"
    - "fighting fake news image splice detection via learned self consistency"
    - "techniques for interpretable machine learning"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "fake news a survey of research detection methods and opportunities"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "scalable and generalizable social bot detection through data selection"):
  with members:
    - "real time event detection on social data streams"
    - "rtbust exploiting temporal patterns for botnet detection on twitter"
    - "its all in a name detecting and labeling bots by their name"
    - "arming the public with artificial intelligence to counter social bots"
    - "bot electioneering volume visualizing social bot activity during elections"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "consistent individualized feature attribution for tree ensembles"
    - "lobo evaluation of generalization deficiencies in twitter bot classifiers"

Connected through hyperedge HE-4 (induced by source paper "the role of bot squads in the political propaganda on twitter"):
  with members:
    - "better safe than sorry an adversarial approach to improve social bot detection"
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"
    - "polarization of the vaccination debate on facebook"
    - "the spread of low credibility content by social bots"
    - "spectrum based deep neural networks for fraud detection"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "holoscope topology and spike aware fraud detection"

Connected through hyperedge HE-5 (induced by source paper "better safe than sorry an adversarial approach to improve social bot detection"):
  with members:
    - "spectrum based deep neural networks for fraud detection"
    - "hardening classifiers against evasion the good the bad and the ugly"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "botivist calling volunteers to action using online bots"
    - "dna inspired online behavioral modeling and its application to spambot detection"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-6 (induced by source paper "rtbust exploiting temporal patterns for botnet detection on twitter"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "relational autoencoder for feature extraction"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "botivist calling volunteers to action using online bots"
    - "botornot a system to evaluate social bots"
    - "dna inspired online behavioral modeling and its application to spambot detection"
    - "the rise of social bots"

Connected through hyperedge HE-7 (induced by source paper "vassl a visual analytics toolkit for social spambot labeling"):
  with members:
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "the rise of social botnets attacks and countermeasures"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-8 (induced by source paper "cashtag piggybacking uncovering spam and bot activity in stock microblogs on twitter"):
  with members:
    - "strategies and influence of social bots in a 2017 german state election a case study on twitter"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "holoscope topology and spike aware fraud detection"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "twitter sentiment around the earnings announcement events"
    - "dna inspired online behavioral modeling and its application to spambot detection"

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)