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: "answerer in questioner s mind information theoretic approach to goal oriented visual dialog"
  Abstract: Goal-oriented dialog has been given attention due to its numerous applications in artificial intelligence. Goal-oriented dialogue tasks occur when a questioner asks an action-oriented question and an answerer responds with the intent of letting the questioner know a correct action to take. To ask the adequate question, deep learning and reinforcement learning have been recently applied. However, these approaches struggle to find a competent recurrent neural questioner, owing to the complexity of learning a series of sentences. Motivated by theory of mind, we propose "Answerer in Questioner's Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog. With AQM, a questioner asks and infers based on an approximated probabilistic model of the answerer. The questioner figures out the answerer's intention via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question. We test our framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and "GuessWhat?!". In our experiments, AQM outperforms comparative algorithms by a large margin.

Connected through hyperedge HE-1 (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"
    - "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"
    - "ask no more deciding when to guess in referential visual dialogue"

Connected through hyperedge HE-2 (induced by source paper "show price and negotiate a hierarchical attention recurrent visual negotiator"):
  with members:
    - "talk the walk navigating new york city through grounded dialogue"
    - "decoupling strategy and generation in negotiation dialogues"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "end to end task completion neural dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "visual dialogue state tracking for question generation"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "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"
    - "ask no more deciding when to guess in referential visual dialogue"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "review based question generation with adaptive instance transfer and augmentation"):
  with members:
    - "dual ask answer network for machine reading comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "two stage synthesis networks for transfer learning in machine comprehension"
    - "neural question generation from text a preliminary study"

Connected through hyperedge HE-5 (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"
    - "leveraging grammar and reinforcement learning for neural program synthesis"
    - "inferring and executing programs for visual reasoning"
    - "attention is all you need"
    - "question asking as program generation"
    - "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-6 (induced by source paper "learning goal oriented visual dialog agents imitating and surpassing analytic experts"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "plato policy learning using adaptive trajectory optimization"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-7 (induced by source paper "informative object annotations tell me something i don t know"):
  with members:
    - "discriminability objective for training descriptive captions"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "efficient human like semantic representations via the information bottleneck principle"
    - "context aware captions from context agnostic supervision"
    - "reasoning about pragmatics with neural listeners and speakers"
    - "salient object detection a benchmark"

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)