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: "knowledge matters importance of prior information for optimization"
  Abstract: We explore the effect of introducing prior information into the intermediate level of neural networks for a learning task on which all the state-of-the-art machine learning algorithms tested failed to learn. We motivate our work from the hypothesis that humans learn such intermediate concepts from other individuals via a form of supervision or guidance using a curriculum. The experiments we have conducted provide positive evidence in favor of this hypothesis. In our experiments, a two-tiered MLP architecture is trained on a dataset with 64x64 binary inputs images, each image with three sprites. The final task is to decide whether all the sprites are the same or one of them is different. Sprites are pentomino tetris shapes and they are placed in an image with different locations using scaling and rotation transformations. The first part of the two-tiered MLP is pre-trained with intermediate-level targets being the presence of sprites at each location, while the second part takes the output of the first part as input and predicts the final task's target binary event. The two-tiered MLP architecture, with a few tens of thousand examples, was able to learn the task perfectly, whereas all other algorithms (include unsupervised pre-training, but also traditional algorithms like SVMs, decision trees and boosting) all perform no better than chance. We hypothesize that the optimization difficulty involved when the intermediate pre-training is not performed is due to the {\em composition} of two highly non-linear tasks. Our findings are also consistent with hypotheses on cultural learning inspired by the observations of optimization problems with deep learning, presumably because of effective local minima.

Connected through hyperedge HE-1 (induced by source paper "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification"):
  with members:
    - "global second order pooling convolutional networks"
    - "yolov3 an incremental improvement"
    - "learning to navigate for fine grained classification"
    - "focal loss for dense object detection"
    - "improved bilinear pooling with cnns"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-2 (induced by source paper "emergence of compositional language with deep generational transmission"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "translating neuralese"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "large scale evolution of image classifiers"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "forced spatial attention for driver foot activity classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "regularized evolution for image classifier architecture search"
    - "looking at the driver rider in autonomous vehicles to predict take over readiness"
    - "tell me where to look guided attention inference network"
    - "learning transferable architectures for scalable image recognition"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-4 (induced by source paper "deep bo for hyperparameter optimization of deep networks"):
  with members:
    - "tapas train less accuracy predictor for architecture search"
    - "bohb robust and efficient hyperparameter optimization at scale"
    - "accelerating neural architecture search using performance prediction"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "parallel predictive entropy search for batch global optimization of expensive objective functions"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-5 (induced by source paper "the futility of bias free learning and search"):
  with members:
    - "when and why metaheuristics researchers can ignore no free lunch theorems"
    - "deep image prior"

Connected through hyperedge HE-6 (induced by source paper "spatiotemporal emotion recognition using deep cnn based on eeg during music listening"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "deep supervision with intermediate concepts"):
  with members:
    - "associative domain adaptation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "unsupervised learning of 3d structure from images"
    - "warpnet weakly supervised matching for single view reconstruction"
    - "deep supervision with shape concepts for occlusion aware 3d object parsing"

Connected through hyperedge HE-8 (induced by source paper "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"):
  with members:
    - "self normalizing neural networks"
    - "adversarial symmetric variational autoencoder"
    - "time series compression based on adaptive piecewise recurrent autoencoder"
    - "relational autoencoder for feature extraction"
    - "autoencoder based holographic image restoration"
    - "medical image denoising using convolutional denoising autoencoders"
    - "variational autoencoders for semi supervised text classification"
    - "generating images with perceptual similarity metrics based on deep 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.LG(Machine Learning)