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: "abstractive summarization of reddit posts with multi level memory networks"
  Abstract: We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model. First, we collect Reddit TIFU dataset, consisting of 120K posts from the online discussion forum Reddit. We use such informal crowd-generated posts as text source, in contrast with existing datasets that mostly use formal documents as source such as news articles. Thus, our dataset could less suffer from some biases that key sentences usually locate at the beginning of the text and favorable summary candidates are already inside the text in similar forms. Second, we propose a novel abstractive summarization model named multi-level memory networks (MMN), equipped with multi-level memory to store the information of text from different levels of abstraction. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the Reddit TIFU dataset is highly abstractive and the MMN outperforms the state-of-the-art summarization models.

Connected through hyperedge HE-1 (induced by source paper "audio driven talking face video generation with learning based personalized head pose"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "neural voice puppetry audio driven facial reenactment"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "lip movements generation at a glance"

Connected through hyperedge HE-2 (induced by source paper "audio driven talking face video generation with natural head pose"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "few shot adversarial learning of realistic neural talking head models"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "unsupervised training for 3d morphable model regression"

Connected through hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "neural text summarization a critical evaluation"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "neural extractive text summarization with syntactic compression"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "annotation artifacts in natural language inference data"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-5 (induced by source paper "pegasus pre training with extracted gap sentences for abstractive summarization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "leafnats an open source toolkit and live demo system for neural abstractive text summarization"
    - "sample efficient text summarization using a single pre trained transformer"
    - "billsum a corpus for automatic summarization of us legislation"
    - "searching for effective neural extractive summarization what works and what s next"
    - "spanbert improving pre training by representing and predicting spans"

Connected through hyperedge HE-6 (induced by source paper "leveraging pre trained checkpoints for sequence generation tasks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "bert rediscovers the classical nlp pipeline"
    - "sample efficient text summarization using a single pre trained transformer"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "mass masked sequence to sequence pre training for language generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "to tune or not to tune how about the best of both worlds"
    - "discofuse a large scale dataset for discourse based sentence fusion"

Connected through hyperedge HE-7 (induced by source paper "in conclusion not repetition comprehensive abstractive summarization with diversified attention based on determinantal point processes"):
  with members:
    - "bigpatent a large scale dataset for abstractive and coherent summarization"
    - "representation degeneration problem in training natural language generation models"
    - "improving abstraction in text summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "bottom up abstractive summarization"
    - "wikihow a large scale text summarization dataset"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "fast greedy map inference for determinantal point process to improve recommendation diversity"

Connected through hyperedge HE-8 (induced by source paper "coloring with limited data few shot colorization via memory augmented networks"):
  with members:
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "coloring with words guiding image colorization through text based palette generation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

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)