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: "direct output connection for a high rank language model"
  Abstract: This paper proposes a state-of-the-art recurrent neural network (RNN) language model that combines probability distributions computed not only from a final RNN layer but also from middle layers. Our proposed method raises the expressive power of a language model based on the matrix factorization interpretation of language modeling introduced by Yang et al. (2018). The proposed method improves the current state-of-the-art language model and achieves the best score on the Penn Treebank and WikiText-2, which are the standard benchmark datasets. Moreover, we indicate our proposed method contributes to two application tasks: machine translation and headline generation. Our code is publicly available at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "transformer xl attentive language models beyond a fixed length context"
    - "backpropagating through structured argmax using a spigot"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "deep residual output layers for neural language generation"):
  with members:
    - "breaking the softmax bottleneck via learnable monotonic pointwise non linearities"
    - "beyond weight tying learning joint input output embeddings for neural machine translation"
    - "zero shot visual recognition using semantics preserving adversarial embedding networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "adaptive noise injection a structure expanding regularization for rnn"):
  with members:
    - "improving neural language modeling via adversarial training"
    - "frage frequency agnostic word representation"
    - "learning to generate reviews and discovering sentiment"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "efficient softmax approximation for gpus"

Connected through hyperedge HE-4 (induced by source paper "breaking the softmax bottleneck via learnable monotonic pointwise non linearities"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "improving neural language models with a continuous cache"

Connected through hyperedge HE-5 (induced by source paper "calibration entropy rates and memory in language models"):
  with members:
    - "frage frequency agnostic word representation"
    - "an analysis of neural language modeling at multiple scales"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "information theoretic co training"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "on calibration of modern neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "topic compositional neural language model"

Connected through hyperedge HE-6 (induced by source paper "exploring kernel functions in the softmax layer for contextual word classification"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "embedding text in hyperbolic spaces"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "positional encoding to control output sequence length"):
  with members:
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "selective encoding for abstractive sentence summarization"
    - "deep recurrent generative decoder for abstractive text summarization"
    - "attention is all you need"
    - "controllable abstractive summarization"
    - "controlling output length in neural encoder decoders"
    - "cutting off redundant repeating generations for neural abstractive summarization"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-8 (induced by source paper "think again networks and the delta loss"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "trellis networks for sequence modeling"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

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)