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: "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing"
  Abstract: We propose a new probabilistic graphical model that jointly models the difficulties of questions, the abilities of participants and the correct answers to questions in aptitude testing and crowdsourcing settings. We devise an active learning/adaptive testing scheme based on a greedy minimization of expected model entropy, which allows a more efficient resource allocation by dynamically choosing the next question to be asked based on the previous responses. We present experimental results that confirm the ability of our model to infer the required parameters and demonstrate that the adaptive testing scheme requires fewer questions to obtain the same accuracy as a static test scenario.

Connected through hyperedge HE-1 (induced by source paper "learning latent parameters without human response patterns item response theory with artificial crowds"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "multi task deep neural networks for natural language understanding"
    - "pyro deep universal probabilistic programming"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "how much reading does reading comprehension require a critical investigation of popular benchmarks"
    - "efficient online scalar annotation with bounded support"
    - "understanding deep learning performance through an examination of test set difficulty a psychometric case study"
    - "building an evaluation scale using item response theory"

Connected through hyperedge HE-2 (induced by source paper "latent distribution assumption for unbiased and consistent consensus modelling"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "label distribution learning"

Connected through hyperedge HE-3 (induced by source paper "graph mining meets crowdsourcing extracting experts for answer aggregation"):
  with members:
    - "the densest subgraph problem with a convex concave size function"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-4 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "peer prediction with heterogeneous tasks"
    - "optimality of belief propagation for crowdsourced classification"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "approval voting and incentives in crowdsourcing"
    - "a survey on truth discovery"

Connected through hyperedge HE-5 (induced by source paper "a bayesian approach for sequence tagging with crowds"):
  with members:
    - "robust argument unit recognition and classification"
    - "leveraging crowdsourcing data for deep active learning an application learning intents in alexa"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "neural architectures for named entity recognition"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-6 (induced by source paper "distinguishing question subjectivity from difficulty for improved crowdsourcing"):
  with members:
    - "crowdsourced task routing via matrix factorization"
    - "crowder crowdsourcing entity resolution"
    - "active learning for crowd sourced databases"

Connected through hyperedge HE-7 (induced by source paper "combining restricted boltzmann machines with neural networks for latent truth discovery"):
  with members:
    - "restricted boltzmann machines for robust and fast latent truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-8 (induced by source paper "cift crowd informed fine tuning to improve machine learning ability"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "hypothesis only baselines in natural language inference"
    - "improving machine learning ability with fine tuning"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "building an evaluation scale using item response theory"
    - "enhanced lstm for natural language inference"
    - "building machines that learn and think like people"
    - "neural semantic encoders"

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)