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: "novelty detection with gan"
  Abstract: The ability of a classifier to recognize unknown inputs is important for many classification-based systems. We discuss the problem of simultaneous classification and novelty detection, i.e. determining whether an input is from the known set of classes and from which specific class, or from an unknown domain and does not belong to any of the known classes. We propose a method based on the Generative Adversarial Networks (GAN) framework. We show that a multi-class discriminator trained with a generator that generates samples from a mixture of nominal and novel data distributions is the optimal novelty detector. We approximate that generator with a mixture generator trained with the Feature Matching loss and empirically show that the proposed method outperforms conventional methods for novelty detection. Our findings demonstrate a simple, yet powerful new application of the GAN framework for the task of novelty detection.

Connected through hyperedge HE-1 (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"
    - "metric learning for novelty and anomaly detection"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "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-2 (induced by source paper "unsupervised out of distribution detection with batch normalization"):
  with members:
    - "neural processes"
    - "are generative deep models for novelty detection truly better"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "group anomaly detection using deep generative models"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-3 (induced by source paper "out of domain detection for natural language understanding in dialog systems"):
  with members:
    - "open world learning and application to product classification"
    - "efficient large scale domain classification with personalized attention"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "learning from positive and unlabeled data a survey"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"
    - "generative probabilistic novelty detection with adversarial autoencoders"

Connected through hyperedge HE-4 (induced by source paper "toward a neuro inspired creative decoder"):
  with members:
    - "inference suboptimality in variational autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "infovae information maximizing variational autoencoders"
    - "artgan artwork synthesis with conditional categorical gans"
    - "a neural representation of sketch drawings"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-5 (induced by source paper "outlier exposure with confidence control for out of distribution detection"):
  with members:
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "deep anomaly detection with outlier exposure"
    - "discriminative out of distribution detection for semantic segmentation"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "open category classification by adversarial sample generation"
    - "sgdr stochastic gradient descent with restarts"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"

Connected through hyperedge HE-6 (induced by source paper "amad adversarial multiscale anomaly detection on high dimensional and time evolving categorical data"):
  with members:
    - "neural related work summarization with a joint context driven attention mechanism"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "adversarially learned anomaly detection"
    - "edge2vec representation learning using edge semantics for biomedical knowledge discovery"
    - "generative adversarial networks an overview"
    - "anomaly detection using one class neural networks"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-7 (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"
    - "metric learning for novelty and anomaly detection"
    - "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-8 (induced by source paper "omnia faster r cnn detection in the wild through dataset merging and soft distillation"):
  with members:
    - "zero shot detection"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combining heterogeneously labeled datasets for training segmentation networks"
    - "reducing overconfident errors outside the known distribution"
    - "open set domain adaptation by backpropagation"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"

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)