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: "image grounded conversations multimodal context for natural question and response generation"
  Abstract: The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations. We present a novel task, Image-Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple-reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialogue research.

Connected through hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "mass masked sequence to sequence pre training for language generation"
    - "disentangling factors of variation using few labels"
    - "learning to select knowledge for response generation in dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog"):
  with members:
    - "image question answer synergistic network for visual dialog"
    - "reasoning visual dialogs with structural and partial observations"
    - "making history matter history advantage sequence training for visual dialog"
    - "dual attention networks for visual reference resolution in visual dialog"
    - "generative visual dialogue system via adaptive reasoning and weighted likelihood estimation"
    - "factor graph attention"
    - "relation aware graph attention network for visual question answering"
    - "flipdial a generative model for two way visual dialogue"

Connected through hyperedge HE-4 (induced by source paper "factor graph attention"):
  with members:
    - "a simple baseline for audio visual scene aware dialog"
    - "diverse and coherent paragraph generation from images"
    - "end to end audio visual scene aware dialog using multimodal attention based video features"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "audio visual scene aware dialog avsd challenge at dstc7"
    - "straight to the facts learning knowledge base retrieval for factual visual question answering"
    - "out of the box reasoning with graph convolution nets for factual visual question answering"
    - "two can play this game visual dialog with discriminative question generation and answering"

Connected through hyperedge HE-5 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "learning to speak and act in a fantasy text adventure game"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "game based video context dialogue"
    - "textworld a learning environment for text based games"
    - "talk the walk navigating new york city through grounded dialogue"
    - "training millions of personalized dialogue agents"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-7 (induced by source paper "my way of telling a story persona based grounded story generation"):
  with members:
    - "contextualize show and tell a neural visual storyteller"
    - "a pipeline for creative visual storytelling"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "style transfer through back translation"
    - "content preserving text generation with attribute controls"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "glac net glocal attention cascading networks for multi image cued story generation"

Connected through hyperedge HE-8 (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"
    - "asking the difficult questions goal oriented visual question generation via intermediate rewards"
    - "learning cooperative visual dialog agents with 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.CL(Computation and Language)