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: "interpretation of feature space using multi channel attentional sub networks"
  Abstract: Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploit a multi-channel attention mechanism in feature space. Our network architecture allows us to obtain an attention mask for each feature while existing CNN visualization methods provide only a common attention mask for all features. We apply the proposed multi-channel attention mechanism to multi-attribute recognition task. We can obtain different attention mask for each feature and for each attribute. Those analyses give us deeper insight into the feature space of CNNs. The experimental results for the benchmark dataset show that the proposed method gives high interpretability to humans while accurately grasping the attributes of the data.

Connected through hyperedge HE-1 (induced by source paper "new perspective of interpretability of deep neural networks"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "channel pruning for accelerating very deep neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "neural architecture search with reinforcement learning"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-2 (induced by source paper "intentional attention mask transformation for robust cnn classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving transparency of deep neural inference process"
    - "visual interpretability for deep learning a survey"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "squeeze and excitation networks"
    - "residual attention network for image classification"
    - "multi scale context aggregation by dilated convolutions"
    - "deep learning face attributes in the wild"

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)