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: "the temporal logic of causal structures"
  Abstract: Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulae with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm's computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.

Connected through hyperedge HE-1 (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"
    - "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"
    - "deep neural networks for bot detection"
    - "early identification of pathogenic social media accounts"

Connected through hyperedge HE-2 (induced by source paper "detecting pathogenic social media accounts without content or network structure"):
  with members:
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"
    - "the darpa twitter bot challenge"
    - "the best answers think twice online detection of commercial campaigns in the cqa forums"
    - "battling the internet water army detection of hidden paid posters"

Connected through hyperedge HE-3 (induced by source paper "an end to end framework to identify pathogenic social media accounts on twitter"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "battling the internet water army detection of hidden paid posters"

Connected through hyperedge HE-4 (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-5 (induced by source paper "open the black box data driven explanation of black box decision systems"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "a survey of methods for explaining black box models"
    - "fairness testing testing software for discrimination"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "exposing the probabilistic causal structure of discrimination"
    - "causal discrimination discovery through propensity score analysis"
    - "why should i trust you explaining the predictions of any classifier"
    - "semantics derived automatically from language corpora necessarily contain human biases"

Connected through hyperedge HE-6 (induced by source paper "causal data science for financial stress testing"):
  with members:
    - "modeling cumulative biological phenomena with suppes bayes causal networks"
    - "efficient computational strategies to learn the structure of probabilistic graphical models of cumulative phenomena"
    - "efficient simulation of financial stress testing scenarios with suppes bayes causal networks"
    - "exposing the probabilistic causal structure of discrimination"

Connected through hyperedge HE-7 (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"
    - "identifying community structures in dynamic networks"

Connected through hyperedge HE-8 (induced by source paper "darkmention a deployed system to predict enterprise targeted external cyberattacks"):
  with members:
    - "economic factors of vulnerability trade and exploitation"
    - "darknet and deepnet mining for proactive cybersecurity threat intelligence"
    - "mining for causal relationships a data driven study of the islamic state"

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.AI(Artificial Intelligence)