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: "identifying and controlling important neurons in neural machine translation"
  Abstract: Neural machine translation (NMT) models learn representations containing substantial linguistic information. However, it is not clear if such information is fully distributed or if some of it can be attributed to individual neurons. We develop unsupervised methods for discovering important neurons in NMT models. Our methods rely on the intuition that different models learn similar properties, and do not require any costly external supervision. We show experimentally that translation quality depends on the discovered neurons, and find that many of them capture common linguistic phenomena. Finally, we show how to control NMT translations in predictable ways, by modifying activations of individual neurons.

Connected through hyperedge HE-1 (induced by source paper "investigating multilingual nmt representations at scale"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "consistency by agreement in zero shot neural machine translation"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "multilingual neural machine translation with language clustering"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "the missing ingredient in zero shot neural machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-2 (induced by source paper "a comparative analysis of knowledge intensive and data intensive semantic parsers"):
  with members:
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "a challenge set for french english machine translation"
    - "analysis methods in neural language processing a survey"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "amr dependency parsing with a typed semantic algebra"

Connected through hyperedge HE-3 (induced by source paper "rapid learning or feature reuse towards understanding the effectiveness of maml"):
  with members:
    - "similarity of neural network representations revisited"
    - "meta learning with differentiable convex optimization"
    - "meta learning representations for continual learning"
    - "few shot text classification with distributional signatures"
    - "a closer look at few shot classification"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "transfusion understanding transfer learning for medical imaging"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "towards deep federated defenses against malware in cloud ecosystems"):
  with members:
    - "how secure is your iot network"
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "deep hyperedges a framework for transductive and inductive learning on hypergraphs"
    - "multi task deep neural networks for natural language understanding"
    - "exbert a visual analysis tool to explore learned representations in transformers models"
    - "hypergraph convolution and hypergraph attention"
    - "what does bert look at an analysis of bert s attention"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-5 (induced by source paper "understanding and controlling memory in recurrent neural networks"):
  with members:
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "the lambada dataset word prediction requiring a broad discourse context"
    - "visualizing and understanding curriculum learning for long short term memory networks"
    - "control of memory active perception and action in minecraft"
    - "end to end memory networks"
    - "visualizing and understanding recurrent networks"
    - "adam a method for stochastic optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-6 (induced by source paper "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"):
  with members:
    - "revisiting the importance of individual units in cnns via ablation"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "learning to generate reviews and discovering sentiment"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "neurox a toolkit for analyzing individual neurons in neural networks"
    - "attention is all you need"
    - "gate activation signal analysis for gated recurrent neural networks and its correlation with phoneme boundaries"
    - "what do neural machine translation models learn about morphology"

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)