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: "exploiting convolution filter patterns for transfer learning"
  Abstract: In this paper, we introduce a new regularization technique for transfer learning. The aim of the proposed approach is to capture statistical relationships among convolution filters learned from a well-trained network and transfer this knowledge to another network. Since convolution filters of the prevalent deep Convolutional Neural Network (CNN) models share a number of similar patterns, in order to speed up the learning procedure, we capture such correlations by Gaussian Mixture Models (GMMs) and transfer them using a regularization term. We have conducted extensive experiments on the CIFAR10, Places2, and CMPlaces datasets to assess generalizability, task transferability, and cross-model transferability of the proposed approach, respectively. The experimental results show that the feature representations have efficiently been learned and transferred through the proposed statistical regularization scheme. Moreover, our method is an architecture independent approach, which is applicable for a variety of CNN architectures.

Connected through hyperedge HE-1 (induced by source paper "towards making deep transfer learning never hurt"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "parameter transfer unit for deep neural networks"
    - "gradient episodic memory for continual learning"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "overcoming catastrophic forgetting in neural networks"
    - "what makes imagenet good for transfer learning"

Connected through hyperedge HE-2 (induced by source paper "delta deep learning transfer using feature map with attention for convolutional networks"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "parameter transfer unit for deep neural networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "overcoming catastrophic forgetting in neural networks"
    - "what makes imagenet good for transfer learning"
    - "learning without forgetting"
    - "deep residual learning for image 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.CV(Computer Vision and Pattern Recognition)