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: "improving reconstruction autoencoder out of distribution detection with mahalanobis distance"
  Abstract: There is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems. A number of recent papers have proposed methods for detecting anomalous image data that appear different from known inlier data samples, including reconstruction-based autoencoders. Autoencoders optimize the compression of input data to a latent space of a dimensionality smaller than the original input and attempt to accurately reconstruct the input using that compressed representation. Since the latent vector is optimized to capture the salient features from the inlier class only, it is commonly assumed that images of objects from outside of the training class cannot effectively be compressed and reconstructed. Some thus consider reconstruction error as a kind of novelty measure. Here we suggest that reconstruction-based approaches fail to capture particular anomalies that lie far from known inlier samples in latent space but near the latent dimension manifold defined by the parameters of the model. We propose incorporating the Mahalanobis distance in latent space to better capture these out-of-distribution samples and our results show that this method often improves performance over the baseline approach.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "data invariants on trust in data driven systems"):
  with members:
    - "tutorial safe and reliable machine learning"
    - "example driven query intent discovery abductive reasoning using semantic similarity"
    - "data sanity check for deep learning systems via learnt assertions"
    - "on calibration of modern neural networks"
    - "learning representations and generative models for 3d point clouds"
    - "on the safety of machine learning cyber physical systems decision sciences and data products"
    - "effective and complete discovery of order dependencies via set based axiomatization"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "out of distribution detection in multi label datasets using latent space of beta vae"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-4 (induced by source paper "detecting out of distribution inputs in deep neural networks using an early layer output"):
  with members:
    - "learning deep features for one class classification"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "on calibration of modern neural networks"
    - "selective classification for deep neural networks"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-5 (induced by source paper "analysis of confident classifiers for out of distribution detection"):
  with members:
    - "a randomized gradient free attack on relu networks"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "denoising autoencoders for overgeneralization in neural networks"
    - "safer classification by synthesis"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-6 (induced by source paper "frodo free rejection of out of distribution samples application to chest x ray analysis"):
  with members:
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "opening the black box of deep neural networks via information"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)