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: "evaluating visual conversational agents via cooperative human ai games"
  Abstract: As AI continues to advance, human-AI teams are inevitable. However, progress in AI is routinely measured in isolation, without a human in the loop. It is crucial to benchmark progress in AI, not just in isolation, but also in terms of how it translates to helping humans perform certain tasks, i.e., the performance of human-AI teams. #R##N#In this work, we design a cooperative game - GuessWhich - to measure human-AI team performance in the specific context of the AI being a visual conversational agent. GuessWhich involves live interaction between the human and the AI. The AI, which we call ALICE, is provided an image which is unseen by the human. Following a brief description of the image, the human questions ALICE about this secret image to identify it from a fixed pool of images. #R##N#We measure performance of the human-ALICE team by the number of guesses it takes the human to correctly identify the secret image after a fixed number of dialog rounds with ALICE. We compare performance of the human-ALICE teams for two versions of ALICE. Our human studies suggest a counterintuitive trend - that while AI literature shows that one version outperforms the other when paired with an AI questioner bot, we find that this improvement in AI-AI performance does not translate to improved human-AI performance. This suggests a mismatch between benchmarking of AI in isolation and in the context of human-AI teams.

Connected through hyperedge HE-1 (induced by source paper "deep bayesian network for visual question generation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "creativity generating diverse questions using variational autoencoders"
    - "what s in a question using visual questions as a form of supervision"
    - "hadamard product for low rank bilinear pooling"
    - "cross modal scene networks"

Connected through hyperedge HE-2 (induced by source paper "guessing state tracking for visual dialogue"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "visual dialogue state tracking for question generation"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "multi step reasoning via recurrent dual attention for visual dialog"
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "an active information seeking model for goal oriented vision and language tasks"
    - "beyond task success a closer look at jointly learning to see ask and guesswhat"

Connected through hyperedge HE-3 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-4 (induced by source paper "efficient attention mechanism for handling all the interactions between many inputs with application to visual dialog"):
  with members:
    - "lxmert learning cross modality encoder representations from transformers"
    - "uniter learning universal image text representations"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"
    - "multi step reasoning via recurrent dual attention for visual dialog"

Connected through hyperedge HE-5 (induced by source paper "large scale answerer in questioner s mind for visual dialog question generation"):
  with members:
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "sliced recurrent neural networks"
    - "mine mutual information neural estimation"
    - "nsml meet the mlaas platform with a real world case study"

Connected through hyperedge HE-6 (induced by source paper "granular multimodal attention networks for visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "rise randomized input sampling for explanation of black box models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "audio visual scene aware dialog"):
  with members:
    - "tvqa localized compositional video question answering"
    - "the activitynet large scale activity recognition challenge 2018 summary"
    - "a dataset for document grounded conversations"
    - "two can play this game visual dialog with discriminative question generation and answering"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dense captioning events in videos"
    - "the kinetics human action video dataset"
    - "aenet learning deep audio features for video analysis"

Connected through hyperedge HE-8 (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"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "learning cooperative visual dialog agents with deep reinforcement 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.HC(Human-Computer Interaction)