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: "training an adaptive dialogue policy for interactive learning of visually grounded word meanings"
  Abstract: We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout the interaction and which ground the meaning representations that it produces. We use this system in interaction with a simulated human tutor to study the effects of different dialogue policies and capabilities on the accuracy of learned meanings, learning rates, and efforts/costs to the tutor. We show that the overall performance of the learning agent is affected by (1) who takes initiative in the dialogues; (2) the ability to express/use their confidence level about visual attributes; and (3) the ability to process elliptical and incrementally constructed dialogue turns. Ultimately, we train an adaptive dialogue policy which optimises the trade-off between classifier accuracy and tutoring costs.

Connected through hyperedge HE-1 (induced by source paper "ask no more deciding when to guess in referential visual dialogue"):
  with members:
    - "image grounded conversations multimodal context for natural question and response generation"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "are you talking to me reasoned visual dialog generation through adversarial learning"
    - "best of both worlds transferring knowledge from discriminative learning to a generative visual dialog model"
    - "deep reinforcement learning for dialogue generation"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "a neural network approach to context sensitive generation of conversational responses"

Connected through hyperedge HE-2 (induced by source paper "reasoning about unforeseen possibilities during policy learning"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "grounding symbols in multi modal instructions"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "the bayesian structural em algorithm"
    - "sequential update of bayesian network structure"
    - "representation learning a review and new perspectives"
    - "reasoning about knowledge of unawareness revisited"
    - "learning to learn"

Connected through hyperedge HE-3 (induced by source paper "teaching machines to describe images via natural language feedback"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "the burchak corpus a challenge data set for interactive learning of visually grounded word meanings"
    - "beating atari with natural language guided reinforcement learning"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "deep reinforcement learning for dialogue generation"
    - "dialog based language learning"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "dialogue learning with human in the loop"

Connected through hyperedge HE-4 (induced by source paper "learning how to learn an adaptive dialogue agent for incrementally learning visually grounded word meanings"):
  with members:
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "bootstrapping incremental dialogue systems using linguistic knowledge to learn from minimal data"
    - "guesswhat visual object discovery through multi modal dialogue"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-5 (induced by source paper "high risk learning acquiring new word vectors from tiny data"):
  with members:
    - "building machines that learn and think like people"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-6 (induced by source paper "the burchak corpus a challenge data set for interactive learning of visually grounded word meanings"):
  with members:
    - "bootstrapping incremental dialogue systems using linguistic knowledge to learn from minimal data"
    - "a sequence to sequence model for user simulation in spoken dialogue systems"

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)