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: "n gram language modeling using recurrent neural network estimation"
  Abstract: We investigate the effective memory depth of RNN models by using them for $n$-gram language model (LM) smoothing. #R##N#Experiments on a small corpus (UPenn Treebank, one million words of training data and 10k vocabulary) have found the LSTM cell with dropout to be the best model for encoding the $n$-gram state when compared with feed-forward and vanilla RNN models. When preserving the sentence independence assumption the LSTM $n$-gram matches the LSTM LM performance for $n=9$ and slightly outperforms it for $n=13$. When allowing dependencies across sentence boundaries, the LSTM $13$-gram almost matches the perplexity of the unlimited history LSTM LM. #R##N#LSTM $n$-gram smoothing also has the desirable property of improving with increasing $n$-gram order, unlike the Katz or Kneser-Ney back-off estimators. Using multinomial distributions as targets in training instead of the usual one-hot target is only slightly beneficial for low $n$-gram orders. #R##N#Experiments on the One Billion Words benchmark show that the results hold at larger scale: while LSTM smoothing for short $n$-gram contexts does not provide significant advantages over classic N-gram models, it becomes effective with long contexts ($n > 5$); depending on the task and amount of data it can match fully recurrent LSTM models at about $n=13$. This may have implications when modeling short-format text, e.g. voice search/query LMs. #R##N#Building LSTM $n$-gram LMs may be appealing for some practical situations: the state in a $n$-gram LM can be succinctly represented with $(n-1)*4$ bytes storing the identity of the words in the context and batches of $n$-gram contexts can be processed in parallel. On the downside, the $n$-gram context encoding computed by the LSTM is discarded, making the model more expensive than a regular recurrent LSTM LM.

Connected through hyperedge HE-1 (induced by source paper "limits of detecting text generated by large scale language models"):
  with members:
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "release strategies and the social impacts of language models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"

Connected through hyperedge HE-2 (induced by source paper "investigation on n gram approximated rnnlms for recognition of morphologically rich speech"):
  with members:
    - "automatic speech recognition with very large conversational finnish and estonian vocabularies"
    - "recurrent neural network regularization"
    - "unsupervised discovery of morphemes"
    - "entropy based pruning of backoff language models"

Connected through hyperedge HE-3 (induced by source paper "sharp nearby fuzzy far away how neural language models use context"):
  with members:
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "contextual lstm clstm models for large scale nlp tasks"
    - "language modeling with gated convolutional networks"
    - "improving neural language models with a continuous cache"
    - "using the output embedding to improve language models"
    - "exploring the limits of language modeling"
    - "visualizing and understanding neural models in nlp"

Connected through hyperedge HE-4 (induced by source paper "what s going on in neural constituency parsers an analysis"):
  with members:
    - "a minimal span based neural constituency parser"
    - "investigating how well contextual features are captured by bi directional recurrent neural network models"
    - "read tag and parse all at once or fully neural dependency parsing"
    - "span based constituency parsing with a structure label system and provably optimal dynamic oracles"
    - "recurrent neural network grammars"
    - "neural architectures for named entity recognition"
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"

Connected through hyperedge HE-5 (induced by source paper "colorless green recurrent networks dream hierarchically"):
  with members:
    - "exploring the syntactic abilities of rnns with multi task learning"
    - "what do neural machine translation models learn about morphology"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "dependency parsing as head selection"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "incremental parsing with minimal features using bi directional lstm"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

Connected through hyperedge HE-6 (induced by source paper "do neural nets learn statistical laws behind natural language"):
  with members:
    - "pointing the unknown words"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "samplernn an unconditional end to end neural audio generation model"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural machine translation in linear time"
    - "wavenet a generative model for raw audio"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "a neural attention model for abstractive sentence summarization"

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)