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 distillation using unlabeled mismatched images"
  Abstract: Current approaches for Knowledge Distillation (KD) either directly use training data or sample from the training data distribution. In this paper, we demonstrate effectiveness of 'mismatched' unlabeled stimulus to perform KD for image classification networks. For illustration, we consider scenarios where this is a complete absence of training data, or mismatched stimulus has to be used for augmenting a small amount of training data. We demonstrate that stimulus complexity is a key factor for distillation's good performance. Our examples include use of various datasets for stimulating MNIST and CIFAR teachers.

Connected through hyperedge HE-1 (induced by source paper "knowledge distillation for optimization of quantized deep neural networks"):
  with members:
    - "relational knowledge distillation"
    - "model compression via distillation and quantization"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "balanced quantization an effective and efficient approach to quantized neural networks"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "towards effective low bitwidth convolutional neural networks"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-2 (induced by source paper "edgeai a vision for deep learning in iot era"):
  with members:
    - "data free knowledge distillation for deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "siamese networks for generating adversarial examples"):
  with members:
    - "practical black box attacks against machine learning"
    - "adversarial examples in the physical world"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "scalable recollections for continual lifelong learning"):
  with members:
    - "automated curriculum learning for neural networks"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "generative adversarial active learning"
    - "continual learning with deep generative replay"
    - "neural episodic control"
    - "learning to remember rare events"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"

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)