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: "choose your neuron incorporating domain knowledge through neuron importance"
  Abstract: Individual neurons in convolutional neural networks supervised for image-level classification tasks have been shown to implicitly learn semantically meaningful concepts ranging from simple textures and shapes to whole or partial objects - forming a "dictionary" of concepts acquired through the learning process. In this work we introduce a simple, efficient zero-shot learning approach based on this observation. Our approach, which we call Neuron Importance-AwareWeight Transfer (NIWT), learns to map domain knowledge about novel "unseen" classes onto this dictionary of learned concepts and then optimizes for network parameters that can effectively combine these concepts - essentially learning classifiers by discovering and composing learned semantic concepts in deep networks. Our approach shows improvements over previous approaches on the CUBirds and AWA2 generalized zero-shot learning benchmarks. We demonstrate our approach on a diverse set of semantic inputs as external domain knowledge including attributes and natural language captions. Moreover by learning inverse mappings, NIWT can provide visual and textual explanations for the predictions made by the newly learned classifiers and provide neuron names. Our code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization"):
  with members:
    - "taking a hint leveraging explanations to make vision and language models more grounded"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "iqa visual question answering in interactive environments"
    - "embodied question answering"
    - "analyzing the behavior of visual question answering models"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-2 (induced by source paper "adapting grad cam for embedding networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hardness aware deep metric learning"
    - "visualizing how embeddings generalize"
    - "improved embeddings with easy positive triplet mining"
    - "divide and conquer the embedding space for metric learning"
    - "visual explanation for deep metric learning"
    - "visualizing deep similarity networks"
    - "deep metric learning beyond binary supervision"

Connected through hyperedge HE-3 (induced by source paper "taking a hint leveraging explanations to make vision and language models more grounded"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "women also snowboard overcoming bias in captioning models"
    - "neural baby talk"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "axiomatic attribution for deep networks"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"

Connected through hyperedge HE-4 (induced by source paper "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"):
  with members:
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "taskonomy disentangling task transfer learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "methods for interpreting and understanding deep neural networks"
    - "do convolutional neural networks learn class hierarchy"
    - "explaining explanations an overview of interpretability of machine learning"
    - "analyzing the noise robustness of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "learning without memorizing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "incremental classifier learning with generative adversarial networks"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "tell me where to look guided attention inference network"
    - "towards continual learning in medical imaging"
    - "memory aware synapses learning what not to forget"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "icarl incremental classifier and representation 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)