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: "question asking as program generation"
  Abstract: A hallmark of human intelligence is the ability to ask rich, creative, and revealing questions. Here we introduce a cognitive model capable of constructing human-like questions. Our approach treats questions as formal programs that, when executed on the state of the world, output an answer. The model specifies a probability distribution over a complex, compositional space of programs, favoring concise programs that help the agent learn in the current context. We evaluate our approach by modeling the types of open-ended questions generated by humans who were attempting to learn about an ambiguous situation in a game. We find that our model predicts what questions people will ask, and can creatively produce novel questions that were not present in the training set. In addition, we compare a number of model variants, finding that both question informativeness and complexity are important for producing human-like questions.

Connected through hyperedge HE-1 (induced by source paper "recent advances in neural question generation"):
  with members:
    - "question generation from sql queries improves neural semantic parsing"
    - "towards a better metric for evaluating question generation systems"
    - "improving neural question generation using answer separation"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "automating reading comprehension by generating question and answer pairs"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "neural generation of diverse questions using answer focus contextual and linguistic features"

Connected through hyperedge HE-2 (induced by source paper "what should i ask using conversationally informative rewards for goal oriented visual dialog"):
  with members:
    - "an active information seeking model for goal oriented vision and language tasks"
    - "learning end to end goal oriented dialog with multiple answers"
    - "jointly learning to see ask and guesswhat"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "modeling question asking using neural program generation"):
  with members:
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "inferring and executing programs for visual reasoning"
    - "attention is all you need"
    - "a syntactic neural model for general purpose code generation"
    - "generalization without systematicity on the compositional skills of sequence to sequence recurrent networks"
    - "generating natural questions about an image"

Connected through hyperedge HE-4 (induced by source paper "weak supervision enhanced generative network for question generation"):
  with members:
    - "neural models for key phrase detection and question generation"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "neural question generation from text a preliminary study"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "language modeling with gated convolutional networks"
    - "ms marco a human generated machine reading comprehension dataset"

Connected through hyperedge HE-5 (induced by source paper "people infer recursive visual concepts from just a few examples"):
  with members:
    - "the omniglot challenge a 3 year progress report"
    - "compositional generalization through meta sequence to sequence learning"
    - "formalizing neurath s ship approximate algorithms for online causal learning"
    - "learning to infer graphics programs from hand drawn images"
    - "building machines that learn and think like people"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "engaging audiences in virtual museums by interactively prompting guiding questions"):
  with members:
    - "learning to ask neural question generation for reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "neural symbolic vqa disentangling reasoning from vision and language understanding"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-8 (induced by source paper "opacity obscurity and the geometry of question asking"):
  with members:
    - "explaining explanations an overview of interpretability of machine learning"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "wrong side of the tracks big data and protected categories"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

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.CL(Computation and Language)