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: "distilling critical paths in convolutional neural networks"
  Abstract: Neural network compression and acceleration are widely demanded currently due to the resource constraints on most deployment targets. In this paper, through analyzing the filter activation, gradients, and visualizing the filters' functionality in convolutional neural networks, we show that the filters in higher layers learn extremely task-specific features, which are exclusive for only a small subset of the overall tasks, or even a single class. Based on such findings, we reveal the critical paths of information flow for different classes. And by their intrinsic property of exclusiveness, we propose a critical path distillation method, which can effectively customize the convolutional neural networks to small ones with much smaller model size and less computation.

Connected through hyperedge HE-1 (induced by source paper "task adaptive incremental learning for intelligent edge devices"):
  with members:
    - "communication efficient learning of deep networks from decentralized data"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "few shot object detection via feature reweighting"):
  with members:
    - "few example object detection with model communication"
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "yolov3 an incremental improvement"
    - "revisiting the importance of individual units in cnns via ablation"
    - "low shot learning from imaginary data"
    - "lstd a low shot transfer detector for object detection"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-3 (induced by source paper "understanding individual neuron importance using information theory"):
  with members:
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "revisiting the importance of individual units in cnns via ablation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "ablation of a robot s brain neural networks under a knife"
    - "estimating information flow in neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "opening the black box of deep neural networks via information"
    - "on extractable shared information"

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)