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: "cost aware pre training for multiclass cost sensitive deep learning"
  Abstract: Deep learning has been one of the most prominent machine learning techniques nowadays, being the state-of-the-art on a broad range of applications where automatic feature extraction is needed. Many such applications also demand varying costs for different types of mis-classification errors, but it is not clear whether or how such cost information can be incorporated into deep learning to improve performance. In this work, we propose a novel cost-aware algorithm that takes into account the cost information into not only the training stage but also the pre-training stage of deep learning. The approach allows deep learning to conduct automatic feature extraction with the cost information effectively. Extensive experimental results demonstrate that the proposed approach outperforms other deep learning models that do not digest the cost information in the pre-training stage.

Connected through hyperedge HE-1 (induced by source paper "on class imbalance and background filtering in visual relationship detection"):
  with members:
    - "graph r cnn for scene graph generation"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "r vqa learning visual relation facts with semantic attention for visual question answering"
    - "visual relationship prediction via label clustering and incorporation of depth information"
    - "image generation from scene graphs"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "vip cnn visual phrase guided convolutional neural network"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "crcen a generalized cost sensitive neural network approach for imbalanced classification"):
  with members:
    - "imbalanced deep learning by minority class incremental rectification"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-3 (induced by source paper "a systematic study of the class imbalance problem in convolutional neural networks"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "the inaturalist species classification and detection dataset"
    - "imagenet pre trained models with batch normalization"
    - "detection of concealed cars in complex cargo x ray imagery using deep learning"
    - "relay backpropagation for effective learning of deep convolutional neural networks"
    - "recent advances in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "cost sensitive deep learning with layer wise cost estimation"):
  with members:
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"
    - "theano based large scale visual recognition with multiple gpus"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "cost sensitive learning of deep feature representations from imbalanced data"):
  with members:
    - "a discriminative representation of convolutional features for indoor scene recognition"
    - "batch normalized maxout network in network"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)