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: "asking the difficult questions goal oriented visual question generation via intermediate rewards"
  Abstract: Despite significant progress in a variety of vision-and-language problems, developing a method capable of asking intelligent, goal-oriented questions about images is proven to be an inscrutable challenge. Towards this end, we propose a Deep Reinforcement Learning framework based on three new intermediate rewards, namely goal-achieved, progressive and informativeness that encourage the generation of succinct questions, which in turn uncover valuable information towards the overall goal. By directly optimizing for questions that work quickly towards fulfilling the overall goal, we avoid the tendency of existing methods to generate long series of insane queries that add little value. We evaluate our model on the GuessWhat?! dataset and show that the resulting questions can help a standard Guesser identify a specific object in an image at a much higher success rate.

Connected through hyperedge HE-1 (induced by source paper "good better best textual distractors generation for multi choice vqa via policy gradient"):
  with members:
    - "augmenting model robustness with transformation invariant attacks"
    - "from recognition to cognition visual commonsense reasoning"
    - "generating natural language adversarial examples"
    - "generating distractors for reading comprehension questions from real examinations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "which training methods for gans do actually converge"
    - "darts deceiving autonomous cars with toxic signs"
    - "dancin seq2seq fooling text classifiers with adversarial text example generation"

Connected through hyperedge HE-2 (induced by source paper "visual dialogue state tracking for question generation"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "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"
    - "ask no more deciding when to guess in referential visual dialogue"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "building task oriented visual dialog systems through alternative optimization between dialog policy and language generation"):
  with members:
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"
    - "sentiment adaptive end to end dialog systems"
    - "dialog based interactive image retrieval"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "multimodal hierarchical reinforcement learning policy for task oriented visual dialog"):
  with members:
    - "feudal reinforcement learning for dialogue management in large domains"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "learning conversational systems that interleave task and non task content"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "sub domain modelling for dialogue management with hierarchical reinforcement learning"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "beyond task success a closer look at jointly learning to see ask and guesswhat"):
  with members:
    - "ask no more deciding when to guess in referential visual dialogue"
    - "interactive reinforcement learning for object grounding via self talking"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "towards end to end learning for dialog state tracking and management using 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.CV(Computer Vision and Pattern Recognition)