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: "leveraging crowdsourcing data for deep active learning an application learning intents in alexa"
  Abstract: This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted crowdsourcing approach, in which multiple annotators of unknown expertise contribute an uncontrolled amount (often limited) of annotations. Specifically, our framework leverages the low-rank structure in annotations to learn individual annotator expertise, which then helps to infer the true labels from noisy and sparse annotations. It provides a unified Bayesian model to simultaneously infer the true labels and train the deep learning model in order to reach an optimal learning efficacy. Finally, our framework exploits the uncertainty of the deep learning model during prediction as well as the annotators’ estimated expertise to minimize the number of required annotations and annotators for optimally training the deep learning model.We evaluate the effectiveness of our framework in both simulated and real-world datasets, and validate its applicability in a strategic task of Alexa, Amazon’s personal assistant, namely intent classification.  Extensive experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches. We further discuss the potential of our proposed framework in bridging machine learning and crowdsourcing towards improved human-in-the-loop systems.

Connected through hyperedge HE-1 (induced by source paper "a human ai loop approach for joint keyword discovery and expectation estimation in micropost event detection"):
  with members:
    - "on human intellect and machine failures troubleshooting integrative machine learning systems"
    - "query expansion with locally trained word embeddings"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "adam a method for stochastic optimization"
    - "the bursty dynamics of the twitter information network"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving active learning on sensitive data for user intent classification"):
  with members:
    - "privacy amplification by subsampling tight analyses via couplings and divergences"
    - "learning privately from multiparty data"
    - "differential privacy and machine learning a survey and review"
    - "calibrating data to sensitivity in private data analysis"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-3 (induced by source paper "a bayesian approach for sequence tagging with crowds"):
  with members:
    - "robust argument unit recognition and classification"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "neural architectures for named entity recognition"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

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.LG(Machine Learning)