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: "cpwc contextual point wise convolution for object recognition"
  Abstract: Convolutional layers are a major driving force behind the successes of deep learning. Pointwise convolution (PWC) is a 1x1 convolutional filter that is primarily used for parameter reduction. However, the PWC ignores the spatial information around the points it is processing. This design is by choice, in order to reduce the overall parameters and computations. However, we hypothesize that this shortcoming of PWC has a significant impact on the network performance. We propose an alternative design for pointwise convolution, which uses spatial information from the input efficiently. Our design significantly improves the performance of the networks without substantially increasing the number of parameters and computations. We experimentally show that our design results in significant improvement in the performance of the network for classification as well as detection.

Connected through hyperedge HE-1 (induced by source paper "a network pruning network approach to deep model compression"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "accuracy booster performance boosting using feature map re calibration"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "leveraging filter correlations for deep model compression"
    - "probabilistic model agnostic meta learning"

Connected through hyperedge HE-2 (induced by source paper "cooperative initialization based deep neural network training"):
  with members:
    - "accuracy booster performance boosting using feature map re calibration"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "leveraging filter correlations for deep model compression"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "accuracy booster performance boosting using feature map re calibration"):
  with members:
    - "cooperative initialization based deep neural network training"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "leveraging filter correlations for deep model compression"
    - "gather excite exploiting feature context in convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-4 (induced by source paper "leveraging filter correlations for deep model compression"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"
    - "bayesian model agnostic meta learning"

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)