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: "identifying unknown unknowns in the open world representations and policies for guided exploration"
  Abstract: Predictive models deployed in the real world may assign incorrect labels to instances with high confidence. Such errors or unknown unknowns are rooted in model incompleteness, and typically arise because of the mismatch between training data and the cases encountered at test time. As the models are blind to such errors, input from an oracle is needed to identify these failures. In this paper, we formulate and address the problem of informed discovery of unknown unknowns of any given predictive model where unknown unknowns occur due to systematic biases in the training data. We propose a model-agnostic methodology which uses feedback from an oracle to both identify unknown unknowns and to intelligently guide the discovery. We employ a two-phase approach which first organizes the data into multiple partitions based on the feature similarity of instances and the confidence scores assigned by the predictive model, and then utilizes an explore-exploit strategy for discovering unknown unknowns across these partitions. We demonstrate the efficacy of our framework by varying the underlying causes of unknown unknowns across various applications. To the best of our knowledge, this paper presents the first algorithmic approach to the problem of discovering unknown unknowns of predictive models.

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 "oodanalyzer interactive analysis of out of distribution samples"):
  with members:
    - "recent research advances on interactive machine learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "imagenet large scale visual recognition challenge"
    - "decaf a deep convolutional activation feature for generic visual recognition"

Connected through hyperedge HE-3 (induced by source paper "exploratory machine learning with unknown unknowns"):
  with members:
    - "an unbiased risk estimator for learning with augmented classes"
    - "fast classification rates without standard margin assumptions"
    - "active feature acquisition with supervised matrix completion"
    - "nearly instance optimal sample complexity bounds for top k arm selection"

Connected through hyperedge HE-4 (induced by source paper "knowledge consistency between neural networks and beyond"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards a unified evaluation of explanation methods without ground truth"
    - "utility analysis of network architectures for 3d point cloud processing"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"

Connected through hyperedge HE-5 (induced by source paper "dynamic neural network decoupling"):
  with members:
    - "yolov3 an incremental improvement"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning efficient convolutional networks through network slimming"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"

Connected through hyperedge HE-6 (induced by source paper "learning from adversarial features for few shot classification"):
  with members:
    - "meta learning for semi supervised few shot classification"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "arcface additive angular margin loss for deep face recognition"
    - "adversarial complementary learning for weakly supervised object localization"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "normface l 2 hypersphere embedding for face verification"

Connected through hyperedge HE-7 (induced by source paper "chip channel wise disentangled interpretation of deep convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "libra r cnn towards balanced learning for object detection"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "mask r cnn"
    - "ask your neurons a deep learning approach to visual question answering"
    - "the mythos of model interpretability"

Connected through hyperedge HE-8 (induced by source paper "characterizing bias in classifiers using generative models"):
  with members:
    - "predictive inequity in object detection"
    - "identifying bias in ai using simulation"
    - "progressive growing of gans for improved quality stability and variation"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning face age progression a pyramid architecture of gans"
    - "lr gan layered recursive generative adversarial networks for image generation"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unsupervised image to image translation with generative adversarial networks"

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.AI(Artificial Intelligence)