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: "reducing network agnostophobia"
  Abstract: Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of interest to them and have to be rejected in order to avoid a false positive. This problem has previously been tackled by researchers by either a) thresholding softmax, which by construction cannot return "none of the known classes", or b) using an additional background or garbage class. In this paper, we show that both of these approaches help, but are generally insufficient when previously unseen classes are encountered. We also introduce a new evaluation metric that focuses on comparing the performance of multiple approaches in scenarios where such unseen classes or unknowns are encountered. Our major contributions are simple yet effective Entropic Open-Set and Objectosphere losses that train networks using negative samples from some classes. These novel losses are designed to maximize entropy for unknown inputs while increasing separation in deep feature space by modifying magnitudes of known and unknown samples. Experiments on networks trained to classify classes from MNIST and CIFAR-10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, NotMNIST, CIFAR-100, and SVHN.

Connected through hyperedge HE-1 (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"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "metric learning for novelty and anomaly detection"
    - "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-2 (induced by source paper "detecting semantic anomalies"):
  with members:
    - "chester a web delivered locally computed chest x ray disease prediction system"
    - "data efficient image recognition with contrastive predictive coding"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep anomaly detection using geometric transformations"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-3 (induced by source paper "controlling risk of web question answering"):
  with members:
    - "densely connected attention propagation for reading comprehension"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "confidence modeling for neural semantic parsing"
    - "know what you don t know unanswerable questions for squad"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "bias reduced uncertainty estimation for deep neural classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "confidence from invariance to image transformations"

Connected through hyperedge HE-4 (induced by source paper "visual and semantic prototypes jointly guided cnn for generalized zero shot learning"):
  with members:
    - "multi modal ensemble classification for generalized zero shot learning"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "robust classification with convolutional prototype learning"
    - "zero shot kernel learning"
    - "towards effective deep embedding for zero shot learning"
    - "bi adversarial auto encoder for zero shot learning"
    - "preserving semantic relations for zero shot learning"

Connected through hyperedge HE-5 (induced by source paper "unified probabilistic deep continual learning through generative replay and open set recognition"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep learning for classical japanese literature"
    - "life long disentangled representation learning with cross domain latent homologies"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "memory replay gans learning to generate images from new categories without forgetting"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-6 (induced by source paper "c2ae class conditioned auto encoder for open set recognition"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "deep transfer learning for multiple class novelty detection"
    - "learning deep features for one class classification"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "dafe fd density aware feature enrichment for face detection"
    - "deep clustering for unsupervised learning of visual features"
    - "deep multimodal subspace clustering networks"

Connected through hyperedge HE-7 (induced by source paper "are out of distribution detection methods effective on large scale datasets"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "deep cnn based multi task learning for open set recognition"
    - "remind your neural network to prevent catastrophic forgetting"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "memory efficient experience replay for streaming learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-8 (induced by source paper "deep transfer learning for multiple class novelty detection"):
  with members:
    - "one class convolutional neural network"
    - "active authentication using an autoencoder regularized cnn based one class classifier"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "c2ae class conditioned auto encoder for open set recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "improving the performance of unimodal dynamic hand gesture recognition with multimodal training"
    - "densely connected convolutional networks"
    - "a baseline for detecting misclassified and out of distribution examples in neural 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.CV(Computer Vision and Pattern Recognition)