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: "learning through dialogue interactions by asking questions"
  Abstract: A good dialogue agent should have the ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction. In this work, we explore this direction by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher. We investigate how a learner can benefit from asking questions in both offline and online reinforcement learning settings, and demonstrate that the learner improves when asking questions. Finally, real experiments with Mechanical Turk validate the approach. Our work represents a first step in developing such end-to-end learned interactive dialogue agents.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-2 (induced by source paper "low resource response generation with template prior"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "text generation with exemplar based adaptive decoding"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "learning neural templates for text generation"
    - "conversational ai the science behind the alexa prize"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-3 (induced by source paper "learning from dialogue after deployment feed yourself chatbot"):
  with members:
    - "beyond turing intelligent agents centered on the user"
    - "bootstrapping conversational agents with weak supervision"
    - "training millions of personalized dialogue agents"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "interactive language acquisition with one shot visual concept learning through a conversational game"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "interconnected question generation with coreference alignment and conversation flow modeling"):
  with members:
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "generating distractors for reading comprehension questions from real examinations"
    - "coqa a conversational question answering challenge"
    - "difficulty controllable generation of reading comprehension questions"
    - "harvesting paragraph level question answer pairs from wikipedia"
    - "learning to ask neural question generation for reading comprehension"
    - "neural question generation from text a preliminary study"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-5 (induced by source paper "listen interact and talk learning to speak via interaction"):
  with members:
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "third person imitation learning"
    - "adversarial learning for neural dialogue generation"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "learning to learn by gradient descent by gradient descent"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-6 (induced by source paper "interactive language acquisition with one shot visual concept learning through a conversational game"):
  with members:
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "third person imitation learning"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "learning language games through interaction"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-7 (induced by source paper "towards an engine for lifelong interactive knowledge learning in human machine conversations"):
  with members:
    - "listen interact and talk learning to speak via interaction"
    - "active one shot learning"
    - "naturalizing a programming language via interactive learning"
    - "a knowledge grounded neural conversation model"
    - "adversarial learning for neural dialogue generation"
    - "data distillation for controlling specificity in dialogue generation"
    - "open world knowledge graph completion"
    - "learning language games through interaction"

Connected through hyperedge HE-8 (induced by source paper "learning to caption images through a lifetime by asking questions"):
  with members:
    - "visual question generation for class acquisition of unknown objects"
    - "visual reasoning by progressive module networks"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "visual question generation as dual task of visual question answering"
    - "learning by asking questions"
    - "teaching machines to describe images via natural language feedback"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "towards diverse and natural image descriptions via a conditional gan"

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)