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: "distance based confidence score for neural network classifiers"
  Abstract: The reliable measurement of confidence in classifiers' predictions is very important for many applications and is, therefore, an important part of classifier design. Yet, although deep learning has received tremendous attention in recent years, not much progress has been made in quantifying the prediction confidence of neural network classifiers. Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with prohibitive computational costs. In this paper we propose a simple, scalable method to achieve a reliable confidence score, based on the data embedding derived from the penultimate layer of the network. We investigate two ways to achieve desirable embeddings, by using either a distance-based loss or Adversarial Training. We then test the benefits of our method when used for classification error prediction, weighting an ensemble of classifiers, and novelty detection. In all tasks we show significant improvement over traditional, commonly used confidence scores.

Connected through hyperedge HE-1 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors"):
  with members:
    - "natural and adversarial error detection using invariance to image transformations"
    - "designing adaptive neural networks for energy constrained image classification"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the fishyscapes benchmark measuring blind spots in semantic segmentation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "fusion volumetric object level slam"
    - "semantic foggy scene understanding with synthetic data"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"):
  with members:
    - "using pre training can improve model robustness and uncertainty"
    - "lightweight probabilistic deep networks"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "confidence scoring using whitebox meta models with linear classifier probes"
    - "arcface additive angular margin loss for deep face recognition"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "accurate uncertainties for deep learning using calibrated regression"

Connected through hyperedge HE-5 (induced by source paper "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-6 (induced by source paper "nn dependability kit engineering neural networks for safety critical autonomous driving systems"):
  with members:
    - "scenic a language for scenario specification and scene generation"
    - "scaling provable adversarial defenses"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "towards dependability metrics for neural networks"
    - "runtime monitoring neuron activation patterns"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

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"
    - "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"
    - "smart mining for deep metric learning"
    - "deep metric learning via facility location"

Connected through hyperedge HE-8 (induced by source paper "active learning for uav based semantic mapping"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "modular sensor fusion for semantic segmentation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "cost effective active learning for deep image classification"

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)