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: "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
  Abstract: Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar—all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives, and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.

Connected through hyperedge HE-1 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing"):
  with members:
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "learning from weak and noisy labels for semantic segmentation"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "deep residual learning for image recognition"
    - "discovering attribute shades of meaning with the crowd"

Connected through hyperedge HE-2 (induced by source paper "decentralized and collaborative ai on blockchain"):
  with members:
    - "trustless machine learning contracts evaluating and exchanging machine learning models on the ethereum blockchain"
    - "universal sentence encoder"
    - "casper the friendly finality gadget"
    - "a collaborative mechanism for crowdsourcing prediction problems"

Connected through hyperedge HE-3 (induced by source paper "citizen science an information quality research frontier"):
  with members:
    - "quizz targeted crowdsourcing with a billion potential users"

Connected through hyperedge HE-4 (induced by source paper "active multi label crowd consensus"):
  with members:
    - "time sensitive bayesian information aggregation for crowdsourcing systems"

Connected through hyperedge HE-5 (induced by source paper "a survey on data collection for machine learning a big data ai integration perspective"):
  with members:
    - "data cleaning for accurate fair and robust models a big data ai integration approach"
    - "the gan landscape losses architectures regularization and normalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "data synthesis based on generative adversarial networks"
    - "a survey on deep transfer learning"
    - "learning based video motion magnification"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-6 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control"):
  with members:
    - "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"
    - "quizz targeted crowdsourcing with a billion potential users"

Connected through hyperedge HE-7 (induced by source paper "a budget feasible peer graded mechanism for iot based crowdsourcing"):
  with members:
    - "a budget feasible mechanism for hiring doctors in e healthcare"
    - "incentives for truthful peer grading"
    - "providing long term participation incentive in participatory sensing"
    - "online assignment of heterogeneous tasks in crowdsourcing markets"
    - "on cost effective incentive mechanisms in microtask crowdsourcing"
    - "budget feasible mechanisms"

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.HC(Human-Computer Interaction)