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: "encoder agnostic adaptation for conditional language generation"
  Abstract: Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a pretrained model for arbitrary downstream tasks. However it is an open-question how to use similar techniques for language generation. Early results in the encoder-agnostic setting have been mostly negative. In this work we explore methods for adapting a pretrained language model to arbitrary conditional input. We observe that pretrained transformer models are sensitive to large parameter changes during tuning. We therefore propose an adaptation that directly injects arbitrary conditioning into self attention, an approach we call pseudo self attention. Through experiments on four diverse conditional text generation tasks we show that this encoder-agnostic technique outperforms strong baselines, produces coherent generations, and is data efficient.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pre training for natural language generation a literature review"):
  with members:
    - "language models as knowledge bases"
    - "denoising based sequence to sequence pre training for text generation"
    - "pre trained language model representations for language generation"
    - "do massively pretrained language models make better storytellers"
    - "mask predict parallel decoding of conditional masked language models"
    - "mass masked sequence to sequence pre training for language generation"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "albert a lite bert for self supervised learning of language representations"

Connected through hyperedge HE-2 (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"
    - "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"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "emotional neural language generation grounded in situational contexts"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "towards best experiment design for evaluating dialogue system output"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "what does bert look at an analysis of bert s attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

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)