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: "punny captions witty wordplay in image descriptions"
  Abstract: Wit is a form of rich interaction that is often grounded in a specific situation (e.g., a comment in response to an event). In this work, we attempt to build computational models that can produce witty descriptions for a given image. Inspired by a cognitive account of humor appreciation, we employ linguistic wordplay, specifically puns, in image descriptions. We develop two approaches which involve retrieving witty descriptions for a given image from a large corpus of sentences, or generating them via an encoder-decoder neural network architecture. We compare our approach against meaningful baseline approaches via human studies and show substantial improvements. We find that when a human is subject to similar constraints as the model regarding word usage and style, people vote the image descriptions generated by our model to be slightly wittier than human-written witty descriptions. Unsurprisingly, humans are almost always wittier than the model when they are free to choose the vocabulary, style, etc.

Connected through hyperedge HE-1 (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-2 (induced by source paper "engaging image captioning via personality"):
  with members:
    - "finding beans in burgers deep semantic visual embedding with localization"
    - "neural baby talk"
    - "training millions of personalized dialogue agents"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "image captioning at will a versatile scheme for effectively injecting sentiments into image descriptions"
    - "neural joking machine humorous image captioning"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-3 (induced by source paper "engaging image chat modeling personality in grounded dialogue"):
  with members:
    - "game based video context dialogue"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "engaging image captioning via personality"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "attention is all you need"
    - "image grounded conversations multimodal context for natural question and response generation"

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)