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 glimpse far into the future understanding long term crowd worker quality"
  Abstract: Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.

Connected through hyperedge HE-1 (induced by source paper "hype a benchmark for human eye perceptual evaluation of generative models"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "unifying human and statistical evaluation for natural language generation"
    - "learning implicit generative models with the method of learned moments"
    - "pros and cons of gan evaluation measures"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "extreme clicking for efficient object annotation"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "click carving segmenting objects in video with point clicks"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "weakly supervised deep detection networks"
    - "fast r cnn"
    - "discovering attribute shades of meaning with the crowd"
    - "analysing domain shift factors between videos and images for object detection"

Connected through hyperedge HE-4 (induced by source paper "crowd guilds worker led reputation and feedback on crowdsourcing platforms"):
  with members:
    - "atelier repurposing expert crowdsourcing tasks as micro internships"
    - "boomerang rebounding the consequences of reputation feedback on crowdsourcing platforms"
    - "thousands of positive reviews distributed mentoring in online fan communities"
    - "laboratories of oligarchy how the iron law extends to peer production"

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)