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: "modeling human reading with neural attention"
  Abstract: When humans read text, they fixate some words and skip others. However, there have been few attempts to explain skipping behavior with computational models, as most existing work has focused on predicting reading times (e.g.,~using surprisal). In this paper, we propose a novel approach that models both skipping and reading, using an unsupervised architecture that combines a neural attention with autoencoding, trained on raw text using reinforcement learning. Our model explains human reading behavior as a tradeoff between precision of language understanding (encoding the input accurately) and economy of attention (fixating as few words as possible). We evaluate the model on the Dundee eye-tracking corpus, showing that it accurately predicts skipping behavior and reading times, is competitive with surprisal, and captures known qualitative features of human reading.

Connected through hyperedge HE-1 (induced by source paper "higher order comparisons of sentence encoder representations"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "correlating neural and symbolic representations of language"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "blackbox meets blackbox representational similarity and stability analysis of neural language models and brains"
    - "symbolic inductive bias for visually grounded learning of spoken language"
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "imitation learning for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"
    - "attention is all you need"
    - "non autoregressive neural machine translation"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-3 (induced by source paper "modeling task effects in human reading with neural attention"):
  with members:
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "show attend and tell neural image caption generation with visual attention"
    - "character aware neural language models"
    - "teaching machines to read and comprehend"
    - "a universal part of speech tagset"

Connected through hyperedge HE-4 (induced by source paper "assessing language proficiency from eye movements in reading"):
  with members:
    - "predicting native language from gaze"
    - "a universal part of speech tagset"

Connected through hyperedge HE-5 (induced by source paper "learning to skim text"):
  with members:
    - "learning recurrent span representations for extractive question answering"
    - "reasonet learning to stop reading in machine comprehension"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a parallel hierarchical model for machine comprehension on sparse data"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "adaptive computation time for recurrent neural networks"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-6 (induced by source paper "neural speed reading via skim rnn"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "learning to skim text"
    - "constructing datasets for multi hop reading comprehension across documents"
    - "recurrent neural network grammars"
    - "strongly typed recurrent neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"):
  with members:
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural headline generation with minimum risk training"
    - "neural summarization by extracting sentences and words"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "generating news headlines with recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "learning neural word salience scores"):
  with members:
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "towards universal paraphrastic sentence embeddings"

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)