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: "distilling information reliability and source trustworthiness from digital traces"
  Abstract: Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their contents. These explicit feedback mechanisms can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy measurements, often biased, to distill a robust, unbiased and interpretable measure of both notions?#R##N##R##N#In this paper, we argue that the large volume of digital traces left by the users within knowledge repositories also reflect information reliability and source trustworthiness. In particular, we propose a temporal point process modeling framework which links the temporal behavior of the users to information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces of the evaluations provided by these users. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.

Connected through hyperedge HE-1 (induced by source paper "intensity free learning of temporal point processes"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "sum of squares polynomial flow"
    - "uncertainty on asynchronous time event prediction"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "deep reinforcement learning of marked temporal point processes"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-2 (induced by source paper "stre self attentive edit quality prediction in wikipedia"):
  with members:
    - "neural based statement classification for biased language"
    - "universal language model fine tuning for text classification"
    - "a structured self attentive sentence embedding"
    - "building automated vandalism detection tools for wikidata"
    - "wikipedia vandal early detection from user behavior to user embedding"
    - "empath understanding topic signals in large scale text"
    - "end to end memory networks"
    - "learning transferable features with deep adaptation networks"

Connected through hyperedge HE-3 (induced by source paper "efficient non parametric bayesian hawkes processes"):
  with members:
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "modeling interdependent and periodic real world action sequences"
    - "feature driven and point process approaches for popularity prediction"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-4 (induced by source paper "joint modeling of event sequence and time series with attentional twin recurrent neural networks"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "multistage campaigning in social networks"
    - "learning granger causality for hawkes processes"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "show attend and tell neural image caption generation with visual attention"
    - "equilibrated adaptive learning rates for non convex optimization"

Connected through hyperedge HE-5 (induced by source paper "fake news mitigation via point process based intervention"):
  with members:
    - "recurrent poisson factorization for temporal recommendation"
    - "wasserstein learning of deep generative point process models"
    - "multistage campaigning in social networks"
    - "steering opinion dynamics in information diffusion networks"
    - "detecting weak changes in dynamic events over networks"
    - "learning granger causality for hawkes processes"
    - "smart broadcasting do you want to be seen"
    - "correlated cascades compete or cooperate"

Connected through hyperedge HE-6 (induced by source paper "fake news detection in social networks via crowd signals"):
  with members:
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "expecting to be hip hawkes intensity processes for social media popularity"
    - "efficient online learning for optimizing value of information theory and application to interactive troubleshooting"
    - "learning to identify ambiguous and misleading news headlines"
    - "discovering valuable items from massive data"
    - "computational fact checking from knowledge networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "tweetcred real time credibility assessment of content on twitter"

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)