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: "it takes two to tango towards theory of ai s mind"
  Abstract: Theory of Mind is the ability to attribute mental states (beliefs, intents, knowledge, perspectives, etc.) to others and recognize that these mental states may differ from one's own. Theory of Mind is critical to effective communication and to teams demonstrating higher collective performance. To effectively leverage the progress in Artificial Intelligence (AI) to make our lives more productive, it is important for humans and AI to work well together in a team. Traditionally, there has been much emphasis on research to make AI more accurate, and (to a lesser extent) on having it better understand human intentions, tendencies, beliefs, and contexts. The latter involves making AI more human-like and having it develop a theory of our minds. In this work, we argue that for human-AI teams to be effective, humans must also develop a theory of AI's mind (ToAIM) - get to know its strengths, weaknesses, beliefs, and quirks. We instantiate these ideas within the domain of Visual Question Answering (VQA). We find that using just a few examples (50), lay people can be trained to better predict responses and oncoming failures of a complex VQA model. We further evaluate the role existing explanation (or interpretability) modalities play in helping humans build ToAIM. Explainable AI has received considerable scientific and popular attention in recent times. Surprisingly, we find that having access to the model's internal states - its confidence in its top-k predictions, explicit or implicit attention maps which highlight regions in the image (and words in the question) the model is looking at (and listening to) while answering a question about an image - do not help people better predict its behavior.

Connected through hyperedge HE-1 (induced by source paper "modeling conceptual understanding in image reference games"):
  with members:
    - "emergence of compositional language with deep generational transmission"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "compositional obverter communication learning from raw visual input"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "faithful multimodal explanation for visual question answering"
    - "interactive agent modeling by learning to probe"

Connected through hyperedge HE-2 (induced by source paper "can you explain that lucid explanations help human ai collaborative image retrieval"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visual interpretability for deep learning a survey"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "manipulating and measuring model interpretability"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "lucid explanations help using a human ai image guessing game to evaluate machine explanation helpfulness"):
  with members:
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "do explanations make vqa models more predictable to a human"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "interpretable explanations of black boxes by meaningful perturbation"

Connected through hyperedge HE-4 (induced by source paper "a surprising density of illusionable natural speech"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "humans can decipher adversarial images"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial attacks on neural network policies"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "robust physical world attacks on machine learning models"

Connected through hyperedge HE-5 (induced by source paper "training machine learning models by regularizing their explanations"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-6 (induced by source paper "modeling others using oneself in multi agent reinforcement learning"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "inverse reward design"

Connected through hyperedge HE-7 (induced by source paper "evaluating theory of mind in question answering"):
  with members:
    - "machine theory of mind"
    - "a simple neural network module for relational reasoning"
    - "who is mistaken"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "tracking the world state with recurrent entity networks"
    - "end to end memory networks"
    - "reasoning in vector space an exploratory study of question answering"
    - "towards ai complete question answering a set of prerequisite toy tasks"

Connected through hyperedge HE-8 (induced by source paper "designing for democratization introducing novices to artificial intelligence via maker kits"):
  with members:
    - "scalable and accurate deep learning for electronic health records"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "caffe convolutional architecture for fast feature embedding"

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)