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: "an empirical study of example forgetting during deep neural network learning"
  Abstract: Inspired by the phenomenon of catastrophic forgetting, we investigate the learning dynamics of neural networks as they train on single classification tasks. Our goal is to understand whether a related phenomenon occurs when data does not undergo a clear distributional shift. We define a `forgetting event' to have occurred when an individual training example transitions from being classified correctly to incorrectly over the course of learning. Across several benchmark data sets, we find that: (i) certain examples are forgotten with high frequency, and some not at all; (ii) a data set's (un)forgettable examples generalize across neural architectures; and (iii) based on forgetting dynamics, a significant fraction of examples can be omitted from the training data set while still maintaining state-of-the-art generalization performance.

Connected through hyperedge HE-1 (induced by source paper "learning and memorizing representative prototypes for 3d point cloud semantic and instance segmentation"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "3d bird s eye view instance segmentation"
    - "kpconv flexible and deformable convolution for point clouds"
    - "associatively segmenting instances and semantics in point clouds"
    - "large scale long tailed recognition in an open world"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "3d instance segmentation via multi task metric learning"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "object detection as a positive unlabeled problem"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"
    - "soft sampling for robust object detection"
    - "positive unlabeled learning with non negative risk estimator"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"

Connected through hyperedge HE-3 (induced by source paper "dissecting catastrophic forgetting in continual learning by deep visualization"):
  with members:
    - "incremental learning techniques for semantic segmentation"
    - "contcap a comprehensive framework for continual image captioning"
    - "a unified framework for lifelong learning in deep neural networks"
    - "toward understanding catastrophic forgetting in continual learning"
    - "lifelong gan continual learning for conditional image generation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "progressive neural networks"
    - "continual learning through synaptic intelligence"

Connected through hyperedge HE-4 (induced by source paper "reducing geographic performance differential for face recognition"):
  with members:
    - "characterizing the variability in face recognition accuracy relative to race"
    - "accuracy comparison across face recognition algorithms where are we on measuring race bias"
    - "identity document to selfie face matching across adolescence"
    - "deep learning is robust to massive label noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "docface matching id document photos to selfies"
    - "docface id document to selfie matching"
    - "additive margin softmax for face verification"

Connected through hyperedge HE-5 (induced by source paper "hyperplane arrangements of trained convnets are biased"):
  with members:
    - "geometry of deep convolutional networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "bounding and counting linear regions of deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-6 (induced by source paper "exploring the memorization generalization continuum in deep learning"):
  with members:
    - "two models of double descent for weak features"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "does learning require memorization a short tale about a long tail"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-7 (induced by source paper "investigating the compositional structure of deep neural networks"):
  with members:
    - "deep neural network ensembles"
    - "not all samples are created equal deep learning with importance sampling"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "explaining explanations an overview of interpretability of machine learning"
    - "learning what data to learn"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "understanding deep learning requires rethinking generalization"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-8 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

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)