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: "deep weighted averaging classifiers"
  Abstract: Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text. Despite these gains, however, concerns have been raised about the calibration, robustness, and interpretability of these models. In this paper we propose a simple way to modify any conventional deep architecture to automatically provide more transparent explanations for classification decisions, as well as an intuitive notion of the credibility of each prediction. Specifically, we draw on ideas from nonparametric kernel regression, and propose to predict labels based on a weighted sum of training instances, where the weights are determined by distance in a learned instance-embedding space. Working within the framework of conformal methods, we propose a new measure of nonconformity suggested by our model, and experimentally validate the accompanying theoretical expectations, demonstrating improved transparency, controlled error rates, and robustness to out-of-domain data, without compromising on accuracy or calibration.

Connected through hyperedge HE-1 (induced by source paper "are graph neural networks miscalibrated"):
  with members:
    - "evaluating model calibration in classification"
    - "fast interpolation based t sne for improved visualization of single cell rna seq data"
    - "fast graph representation learning with pytorch geometric"
    - "pitfalls of graph neural network evaluation"
    - "graph networks as learnable physics engines for inference and control"
    - "a new loss function for temperature scaling to have better calibrated deep networks"
    - "relational inductive biases deep learning and graph networks"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-2 (induced by source paper "how case based reasoning explained neural networks an xai survey of post hoc explanation by example in ann cbr twins"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining explanations in ai"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "interpreting neural networks with nearest neighbors"
    - "yasenn explaining neural networks via partitioning activation sequences"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"

Connected through hyperedge HE-3 (induced by source paper "detecting extrapolation with local ensembles"):
  with members:
    - "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"

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)