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: "selective classification for deep neural networks"
  Abstract: Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, and almost 60% test coverage.

Connected through hyperedge HE-1 (induced by source paper "utilizing network properties to detect erroneous inputs"):
  with members:
    - "looking ahead anticipating pedestrians crossing with future frames prediction"
    - "adversarial examples are a natural consequence of test error in noise"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "natural adversarial examples"
    - "selectivenet a deep neural network with an integrated reject option"
    - "improved adversarial robustness by reducing open space risk via tent activations"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-3 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation"):
  with members:
    - "a benchmark for anomaly segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "mask scoring r cnn"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "an alarm system for segmentation algorithm based on shape model"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "anomaly detection using deep learning based image completion"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "practical solutions for machine learning safety in autonomous vehicles"):
  with members:
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"
    - "selectivenet a deep neural network with an integrated reject option"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "using pre training can improve model robustness and uncertainty"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "training verified learners with learned verifiers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-6 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"

Connected through hyperedge HE-7 (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"
    - "reducing network agnostophobia"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "thanks for nothing predicting zero valued activations with lightweight convolutional neural networks"):
  with members:
    - "divide and conquer leveraging intermediate feature representations for quantized training of neural networks"
    - "spatial correlation and value prediction in convolutional neural networks"
    - "efficient and robust parallel dnn training through model parallelism on multi gpu platform"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "an analysis of deep neural network models for practical applications"
    - "sparsenn an energy efficient neural network accelerator exploiting input and output sparsity"
    - "efficient processing of deep neural networks a tutorial and survey"

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)