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: "cold fusion training seq2seq models together with language models"
  Abstract: Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition. Performance has further been improved by leveraging unlabeled data, often in the form of a language model. In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task. We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.

Connected through hyperedge HE-1 (induced by source paper "object relational graph with teacher recommended learning for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "controllable video captioning with pos sequence guidance based on gated fusion network"
    - "memory attended recurrent network for video captioning"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "relation aware graph attention network for visual question answering"
    - "less is more picking informative frames for video captioning"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "can unconditional language models recover arbitrary sentences"):
  with members:
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "measuring the intrinsic dimension of objective landscapes"
    - "a stable and effective learning strategy for trainable greedy decoding"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "pseudolikelihood reranking with masked language models"):
  with members:
    - "passage re ranking with bert"
    - "effective sentence scoring method using bidirectional language model for speech recognition"
    - "pre trained language model representations for language generation"
    - "bertscore evaluating text generation with bert"
    - "transformer xl attentive language models beyond a fixed length context"
    - "language models with transformers"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "language modeling with deep transformers"

Connected through hyperedge HE-4 (induced by source paper "encoder agnostic adaptation for conditional language generation"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "pre trained language model representations for language generation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "language models with transformers"
    - "cross lingual language model pretraining"
    - "an embarrassingly simple approach for transfer learning from pretrained language models"
    - "unified language model pre training for natural language understanding and generation"

Connected through hyperedge HE-5 (induced by source paper "structured fusion networks for dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "pretraining methods for dialog context representation learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "zero shot dialog generation with cross domain latent actions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "cross domain dialogue policy transfer via simultaneous speech act and slot alignment"

Connected through hyperedge HE-6 (induced by source paper "deep neural network for semantic based text recognition in images"):
  with members:
    - "global encoding for abstractive summarization"
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "edit probability for scene text recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "neural networks for text correction and completion in keyboard decoding"
    - "protest activity detection and perceived violence estimation from social media images"
    - "text attentional convolutional neural network for scene text detection"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-7 (induced by source paper "do massively pretrained language models make better storytellers"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "the curious case of neural text degeneration"
    - "improving neural story generation by targeted common sense grounding"
    - "unifying human and statistical evaluation for natural language generation"
    - "encoder agnostic adaptation for conditional language generation"
    - "gltr statistical detection and visualization of generated text"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "importance of search and evaluation strategies in neural dialogue modeling"

Connected through hyperedge HE-8 (induced by source paper "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"):
  with members:
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "neural text generation past present and beyond"
    - "deep text classification can be fooled"
    - "simple fusion return of the language model"

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)