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: "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
  Abstract: Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For example, overall performance of a cancer detection model may be high, but the model still consistently misses a rare but aggressive cancer subtype. We refer to this problem as hidden stratification, and observe that it results from incompletely describing the meaningful variation in a dataset. While hidden stratification can substantially reduce the clinical efficacy of machine learning models, its effects remain difficult to measure. In this work, we assess the utility of several possible techniques for measuring and describing hidden stratification effects, and characterize these effects on multiple medical imaging datasets. We find evidence that hidden stratification can occur in unidentified imaging subsets with low prevalence, low label quality, subtle distinguishing features, or spurious correlates, and that it can result in relative performance differences of over 20% on clinically important subsets. Finally, we explore the clinical implications of our findings, and suggest that evaluation of hidden stratification should be a critical component of any machine learning deployment in medical imaging.

Connected through hyperedge HE-1 (induced by source paper "it s not what machines can learn it s what we cannot teach"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "generating difficult sat instances by preventing triangles"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "guiding high performance sat solvers with unsat core predictions"
    - "a survey on bias and fairness in machine learning"
    - "deep learning for symbolic mathematics"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "diversity sampling is an implicit regularization for kernel methods"):
  with members:
    - "does learning require memorization a short tale about a long tail"
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-3 (induced by source paper "distributionally robust neural networks for group shifts on the importance of regularization for worst case generalization"):
  with members:
    - "invariance inducing regularization using worst case transformations suffices to boost accuracy and spatial robustness"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "distributionally robust language modeling"
    - "class balanced loss based on effective number of samples"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "annotation artifacts in natural language inference data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "slice based learning a programming model for residual learning in critical data slices"):
  with members:
    - "overton a data system for monitoring and improving machine learned products"
    - "wic the word in context dataset for evaluating context sensitive meaning representations"
    - "probing what different nlp tasks teach machines about function word comprehension"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "snorkel drybell a case study in deploying weak supervision at industrial scale"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural network acceptability judgments"

Connected through hyperedge HE-5 (induced by source paper "who wants accurate models arguing for a different metrics to take classification models seriously"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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.LG(Machine Learning)