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: "a dataset of peer reviews peerread collection insights and nlp applications"
  Abstract: Peer reviewing is a central component in the scientific publishing process. We present the first public dataset of scientific peer reviews available for research purposes (PeerRead v1) providing an opportunity to study this important artifact. The dataset consists of 14.7K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR. The dataset also includes 10.7K textual peer reviews written by experts for a subset of the papers. We describe the data collection process and report interesting observed phenomena in the peer reviews. We also propose two novel NLP tasks based on this dataset and provide simple baseline models. In the first task, we show that simple models can predict whether a paper is accepted with up to 21% error reduction compared to the majority baseline. In the second task, we predict the numerical scores of review aspects and show that simple models can outperform the mean baseline for aspects with high variance such as 'originality' and 'impact'.

Connected through hyperedge HE-1 (induced by source paper "textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome"):
  with members:
    - "model free model based and general intelligence"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-3 (induced by source paper "evaluation benchmarks and learning criteriafor discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"
    - "discourse aware neural rewards for coherent text generation"
    - "collecting diverse natural language inference problems for sentence representation evaluation"

Connected through hyperedge HE-4 (induced by source paper "earlier isn t always better sub aspect analysis on corpus and system biases in summarization"):
  with members:
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "a simple theoretical model of importance for summarization"
    - "toward abstractive summarization using semantic representations"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "er ae differentially private text generation for authorship anonymization"):
  with members:
    - "author obfuscation using generalised differential privacy"
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial text generation via feature mover s distance"
    - "universal sentence encoder"
    - "content preserving text generation with attribute controls"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "linguistic versus latent relations for modeling coherent flow in paragraphs"):
  with members:
    - "describing a knowledge base"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "detecting and explaining causes from text for a time series event"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "variational neural discourse relation recognizer"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"

Connected through hyperedge HE-7 (induced by source paper "a joint model for multimodal document quality assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "machine translation evaluation meets community question answering"
    - "deep paper gestalt"
    - "automatic academic paper rating based on modularized hierarchical convolutional neural network"
    - "skipflow incorporating neural coherence features for end to end automatic text scoring"
    - "automatic text scoring using neural networks"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "back to the future sequential alignment of text representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a review of domain adaptation without target labels"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rumoureval 2019 determining rumour veracity and support for rumours"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "attention is all you need"

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)