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: "metric learning for novelty and anomaly detection"
  Abstract: When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works.

Connected through hyperedge HE-1 (induced by source paper "multi class novelty detection using mix up technique"):
  with members:
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "hierarchical novelty detection for visual object recognition"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data"):
  with members:
    - "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"
    - "moment matching for multi source domain adaptation"
    - "reducing network agnostophobia"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-3 (induced by source paper "image based ood detector principles on graph based input data in human action recognition"):
  with members:
    - "human action recognition and prediction a survey"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "novelty detection with gan"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-4 (induced by source paper "selective brain damage measuring the disparate impact of model pruning"):
  with members:
    - "natural adversarial examples"
    - "a benchmark for interpretability methods in deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "efficient neural audio synthesis"
    - "snip single shot network pruning based on connection sensitivity"

Connected through hyperedge HE-5 (induced by source paper "learning metrics from teachers compact networks for image embedding"):
  with members:
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "learning deep representations with probabilistic knowledge transfer"
    - "deep mutual learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "deep image retrieval learning global representations for image search"

Connected through hyperedge HE-6 (induced by source paper "the importance of metric learning for robotic vision open set recognition and active learning"):
  with members:
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "novelty detection with gan"
    - "generative adversarial active learning"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "distance based confidence score for neural network classifiers"
    - "smart mining for deep metric learning"
    - "deep metric learning via facility location"

Connected through hyperedge HE-7 (induced by source paper "uninformed students student teacher anomaly detection with discriminative latent embeddings"):
  with members:
    - "improving unsupervised defect segmentation by applying structural similarity to autoencoders"
    - "deep transfer learning for multiple class novelty detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "do better imagenet models transfer better"
    - "informed democracy voting based novelty detection for action recognition"
    - "are pre trained cnns good feature extractors for anomaly detection in surveillance videos"
    - "latent space autoregression for novelty detection"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"

Connected through hyperedge HE-8 (induced by source paper "an explanation method for siamese neural networks"):
  with members:
    - "evaluating explainers via perturbation"
    - "explaining deep neural networks with a polynomial time algorithm for shapley values approximation"
    - "alime autoencoder based approach for local interpretability"
    - "dlime a deterministic local interpretable model agnostic explanations approach for computer aided diagnosis systems"
    - "interpretable counterfactual explanations guided by prototypes"
    - "normlime a new feature importance metric for explaining deep neural networks"
    - "model agnostic contrastive explanations for structured data"
    - "a survey of evaluation methods and measures for interpretable machine learning"

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.CV(Computer Vision and Pattern Recognition)