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: "quantifying layerwise information discarding of neural networks"
  Abstract: This paper presents a method to explain how input information is discarded through intermediate layers of a neural network during the forward propagation, in order to quantify and diagnose knowledge representations of pre-trained deep neural networks. We define two types of entropy-based metrics, i.e., the strict information discarding and the reconstruction uncertainty, which measure input information of a specific layer from two perspectives. We develop a method to enable efficient computation of such entropy-based metrics. Our method can be broadly applied to various neural networks and enable comprehensive comparisons between different layers of different networks. Preliminary experiments have shown the effectiveness of our metrics in analyzing benchmark networks and explaining existing deep-learning techniques.

Connected through hyperedge HE-1 (induced by source paper "explaining knowledge distillation by quantifying the knowledge"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "stiffness a new perspective on generalization in neural networks"
    - "transferring knowledge across learning processes"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "understanding training and generalization in deep learning by fourier analysis"
    - "i revnet deep invertible networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-2 (induced by source paper "utility analysis of network architectures for 3d point cloud processing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a cnn annularly convolutional neural networks on point clouds"
    - "similarity of neural network representations revisited"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "3d point capsule networks"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "multiresolution tree networks for 3d point cloud processing"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "knowledge consistency between neural networks and beyond"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "towards a unified evaluation of explanation methods without ground truth"
    - "utility analysis of network architectures for 3d point cloud processing"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-4 (induced by source paper "explaining neural networks semantically and quantitatively"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting cnns via decision trees"
    - "unsupervised learning of neural networks to explain neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interpreting cnn knowledge via an explanatory graph"

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)