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: "incentivizing high quality crowdwork"
  Abstract: We study the causal effects of financial incentives on the quality of crowdwork. We focus on performance-based payments (PBPs), bonus payments awarded to workers for producing high quality work. We design and run randomized behavioral experiments on the popular crowdsourcing platform Amazon Mechanical Turk with the goal of understanding when, where, and why PBPs help, identifying properties of the payment, payment structure, and the task itself that make them most effective. We provide examples of tasks for which PBPs do improve quality. For such tasks, the effectiveness of PBPs is not too sensitive to the threshold for quality required to receive the bonus, while the magnitude of the bonus must be large enough to make the reward salient. We also present examples of tasks for which PBPs do not improve quality. Our results suggest that for PBPs to improve quality, the task must be effort-responsive: the task must allow workers to produce higher quality work by exerting more effort. We also give a simple method to determine if a task is effort-responsive a priori. Furthermore, our experiments suggest that all payments on Mechanical Turk are, to some degree, implicitly performance-based in that workers believe their work may be rejected if their performance is sufficiently poor. Finally, we propose a new model of worker behavior that extends the standard principal-agent model from economics to include a worker's subjective beliefs about his likelihood of being paid, and show that the predictions of this model are in line with our experimental findings. This model may be useful as a foundation for theoretical studies of incentives in crowdsourcing markets.

Connected through hyperedge HE-1 (induced by source paper "crowdsourcing the perception of machine teaching"):
  with members:
    - "diversity in machine learning"
    - "the challenge of crafting intelligible intelligence"
    - "trick me if you can human in the loop generation of adversarial examples for question answering"
    - "meta transfer learning for few shot learning"
    - "an overview of machine teaching"
    - "understanding hand object manipulation by modeling the contextual relationship between actions grasp types and object attributes"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "machine teaching a new paradigm for building machine learning systems"

Connected through hyperedge HE-2 (induced by source paper "mapping the potential and pitfalls of data dividends as a means of sharing the profits of artificial intelligence"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "low cost learning via active data procurement"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-3 (induced by source paper "large scale organization and inference of an imagery dataset for public safety"):
  with members:
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "high throughput ingest of provenance records into accumulo"
    - "youtube 8m a large scale video classification benchmark"
    - "lustre hadoop accumulo"
    - "imagenet large scale visual recognition challenge"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-4 (induced by source paper "practice of efficient data collection via crowdsourcing at large scale"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "approval voting and incentives in crowdsourcing"
    - "how many workers to ask adaptive exploration for collecting high quality labels"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "learning to predict the wisdom of crowds"

Connected through hyperedge HE-5 (induced by source paper "heuristics in multi winner approval voting"):
  with members:
    - "modeling peoples voting behavior with poll information"
    - "a local dominance theory of voting equilibria"
    - "computational aspects of multi winner approval voting"
    - "how hard is it to control an election by breaking ties"

Connected through hyperedge HE-6 (induced by source paper "flud a hybrid crowd algorithm approach for visualizing biological networks"):
  with members:
    - "what would a graph look like in this layout a machine learning approach to large graph visualization"
    - "drawing arrangement graphs in small grids or how to play planarity"

Connected through hyperedge HE-7 (induced by source paper "attention aware answers of the crowd"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "cdas a crowdsourcing data analytics system"

Connected through hyperedge HE-8 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"
    - "near optimally teaching the crowd to classify"

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.GT(Computer Science and Game Theory)