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: "why self attention a targeted evaluation of neural machine translation architectures"
  Abstract: Recently, non-recurrent architectures (convolutional, self-attentional) have outperformed RNNs in neural machine translation. CNNs and self-attentional networks can connect distant words via shorter network paths than RNNs, and it has been speculated that this improves their ability to model long-range dependencies. However, this theoretical argument has not been tested empirically, nor have alternative explanations for their strong performance been explored in-depth. We hypothesize that the strong performance of CNNs and self-attentional networks could also be due to their ability to extract semantic features from the source text, and we evaluate RNNs, CNNs and self-attention networks on two tasks: subject-verb agreement (where capturing long-range dependencies is required) and word sense disambiguation (where semantic feature extraction is required). Our experimental results show that: 1) self-attentional networks and CNNs do not outperform RNNs in modeling subject-verb agreement over long distances; 2) self-attentional networks perform distinctly better than RNNs and CNNs on word sense disambiguation.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving neural language modeling via adversarial training"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"

Connected through hyperedge HE-2 (induced by source paper "time aware large kernel convolutions"):
  with members:
    - "accelerating large kernel convolution using summed area tables"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "massively multilingual neural machine translation"
    - "fast parametric learning with activation memorization"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "fixed encoder self attention patterns in transformer based machine translation"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "linguistic knowledge and transferability of contextual representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "what can linguistics and deep learning contribute to each other"
    - "what do rnn language models learn about filler gap dependencies"
    - "what s going on in neural constituency parsers an analysis"
    - "language modeling teaches you more syntax than translation does lessons learned through auxiliary task analysis"
    - "can you tell me how to get past sesame street sentence level pretraining beyond language modeling"

Connected through hyperedge HE-5 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "modeling recurrence for transformer"
    - "towards robust neural machine translation"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "pay less attention with lightweight and dynamic convolutions"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "scaling neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "smoothed dilated convolutions for improved dense prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "bert4rec sequential recommendation with bidirectional encoder representations from transformer"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "multi head attention with disagreement regularization"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"

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)