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: "modelling visualising and summarising documents with a single convolutional neural network"
  Abstract: Capturing the compositional process which maps the meaning of words to that of documents is a central challenge for researchers in Natural Language Processing and Information Retrieval. We introduce a model that is able to represent the meaning of documents by embedding them in a low dimensional vector space, while preserving distinctions of word and sentence order crucial for capturing nuanced semantics. Our model is based on an extended Dynamic Convolution Neural Network, which learns convolution filters at both the sentence and document level, hierarchically learning to capture and compose low level lexical features into high level semantic concepts. We demonstrate the effectiveness of this model on a range of document modelling tasks, achieving strong results with no feature engineering and with a more compact model. Inspired by recent advances in visualising deep convolution networks for computer vision, we present a novel visualisation technique for our document networks which not only provides insight into their learning process, but also can be interpreted to produce a compelling automatic summarisation system for texts.

Connected through hyperedge HE-1 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised domain detection"):
  with members:
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "attention is all you need"
    - "multiple instance learning networks for fine grained sentiment analysis"
    - "variations of the similarity function of textrank for automated summarization"
    - "very deep convolutional networks for text classification"
    - "rationale augmented convolutional neural networks for text classification"
    - "domain separation networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "open domain event extraction and embedding for natural gas market prediction"):
  with members:
    - "forecasting economics and financial time series arima vs lstm"
    - "cevo comprehensive event ontology enhancing cognitive annotation"
    - "temporal attention augmented bilinear network for financial time series data analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "insight 1 at semeval 2016 task 5 deep learning for multilingual aspect based sentiment analysis"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "zero shot sequence labeling transferring knowledge from sentences to tokens"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "automatic text scoring using neural networks"
    - "neural architectures for named entity recognition"
    - "a neural attention model for abstractive sentence summarization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "task specific word identification from short texts using a convolutional neural network"):
  with members:
    - "feature selection a data perspective"
    - "long short term memory networks for machine reading"
    - "visualizing and understanding neural models in nlp"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "teaching machines to read and comprehend"
    - "learning deep features for discriminative localization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "dynamic compositional neural networks over tree structure"):
  with members:
    - "learning feed forward one shot learners"
    - "self adaptive hierarchical sentence model"
    - "improved semantic representations from tree structured long short term memory networks"
    - "a large annotated corpus for learning natural language inference"
    - "recursive neural networks can learn logical semantics"
    - "convolutional neural networks for sentence classification"
    - "a convolutional neural network for modelling sentences"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-7 (induced by source paper "deep learning applied to nlp"):
  with members:
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "comparing convolutional neural networks to traditional models for slot filling"
    - "dependency based convolutional neural networks for sentence embedding"
    - "a re ranking model for dependency parser with recursive convolutional neural network"

Connected through hyperedge HE-8 (induced by source paper "a convolutional attention network for extreme summarization of source code"):
  with members:
    - "a neural attention model for abstractive sentence summarization"
    - "neural programmer inducing latent programs with gradient descent"
    - "learning program embeddings to propagate feedback on student code"
    - "transition based dependency parsing with stack long short term memory"
    - "learning to transduce with unbounded memory"
    - "neural programmer interpreters"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "inferring algorithmic patterns with stack augmented recurrent nets"

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)