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: "embracing error to enable rapid crowdsourcing"
  Abstract: Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data. To scale and widen the applicability of crowdsourcing, we present a technique that produces extremely rapid judgments for binary and categorical labels. Rather than punishing all errors, which causes workers to proceed slowly and deliberately, our technique speeds up workers' judgments to the point where errors are acceptable and even expected. We demonstrate that it is possible to rectify these errors by randomizing task order and modeling response latency. We evaluate our technique on a breadth of common labeling tasks such as image verification, word similarity, sentiment analysis and topic classification. Where prior work typically achieves a 0.25x to 1x speedup over fixed majority vote, our approach often achieves an order of magnitude (10x) speedup.

Connected through hyperedge HE-1 (induced by source paper "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "how does disagreement help generalization against label corruption"
    - "on exact computation with an infinitely wide neural net"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-2 (induced by source paper "human uncertainty makes classification more robust"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "deep learning is robust to massive label noise"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "mixup beyond empirical risk minimization"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "shake shake regularization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "efficient object annotation via speaking and pointing"):
  with members:
    - "natural vocabulary emerges from free form annotations"
    - "scaling egocentric vision the epic kitchens dataset"
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "fast object class labelling via speech"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "object referring in visual scene with spoken language"

Connected through hyperedge HE-5 (induced by source paper "robust metric learning based on the rescaled hinge loss"):
  with members:
    - "sparse compositional metric learning"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "fast object class labelling via speech"):
  with members:
    - "where are the blobs counting by localization with point supervision"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "jointly discovering visual objects and spoken words from raw sensory input"
    - "pathtrack fast trajectory annotation with path supervision"
    - "training object class detectors with click supervision"
    - "spot on action localization from pointly supervised proposals"
    - "what s the point semantic segmentation with point supervision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "c wsl count guided weakly supervised localization"):
  with members:
    - "focal loss for dense object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "an analysis of scale invariance in object detection snip"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"

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)