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: "a data driven analysis of workers earnings on amazon mechanical turk"
  Abstract: A growing number of people are working as part of on-line crowd work, which has been characterized by its low wages; yet, we know little about wage distribution and causes of low/high earnings. We recorded 2,676 workers performing 3.8 million tasks on Amazon Mechanical Turk. Our task-level analysis revealed that workers earned a median hourly wage of only ~\$2/h, and only 4% earned more than \$7.25/h. The average requester pays more than \$11/h, although lower-paying requesters post much more work. Our wage calculations are influenced by how unpaid work is included in our wage calculations, e.g., time spent searching for tasks, working on tasks that are rejected, and working on tasks that are ultimately not submitted. We further explore the characteristics of tasks and working patterns that yield higher hourly wages. Our analysis informs future platform design and worker tools to create a more positive future for crowd work.

Connected through hyperedge HE-1 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "interactive full image segmentation by considering all regions jointly"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to segment via cut and paste"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual affordance and function understanding a survey"

Connected through hyperedge HE-2 (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"
    - "machine teaching a new paradigm for building machine learning systems"
    - "becoming the expert interactive multi class machine teaching"

Connected through hyperedge HE-3 (induced by source paper "developing a personality model for speech based conversational agents using the psycholexical approach"):
  with members:
    - "mapping perceptions of humanness in intelligent personal assistant interaction"
    - "latent human traits in the language of social media an open vocabulary approach"

Connected through hyperedge HE-4 (induced by source paper "the photobook dataset building common ground through visually grounded dialogue"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "modulating early visual processing by language"
    - "context aware captions from context agnostic supervision"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "a survey of crowdsourcing in medical image analysis"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "crowd disagreement of medical images is informative"
    - "crowd assisted polyp annotation of virtual colonoscopy videos"
    - "employing weak annotations for medical image analysis problems"
    - "expected exponential loss for gaze based video and volume ground truth annotation"
    - "negative results in computer vision a perspective"
    - "a survey on deep learning in medical image analysis"
    - "crowdsourcing for identification of polyp free segments in virtual colonoscopy videos"

Connected through hyperedge HE-6 (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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "low cost learning via active data procurement"
    - "incentivizing high quality crowdwork"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-7 (induced by source paper "auditing news curation systems a case study examining algorithmic and editorial logic in apple news"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "semantics derived automatically from language corpora contain human like biases"
    - "analytic methods for optimizing realtime crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "turkscanner predicting the hourly wage of microtasks"):
  with members:
    - "crowd coach peer coaching for crowd workers skill growth"
    - "xgboost a scalable tree boosting system"
    - "the labor economics of paid crowdsourcing"
    - "the condition of the turking class are online employers fair and honest"

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.CY(Computers and Society)