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: "attending to mathematical language with transformers"
  Abstract: Mathematical expressions were generated, evaluated and used to train neural network models based on the transformer architecture. The expressions and their targets were analyzed as a character-level sequence transduction task in which the encoder and decoder are built on attention mechanisms. Three models were trained to understand and evaluate symbolic variables and expressions in mathematics: (1) the self-attentive and feed-forward transformer without recurrence or convolution, (2) the universal transformer with recurrence, and (3) the adaptive universal transformer with recurrence and adaptive computation time. The models respectively achieved test accuracies as high as 76.1%, 78.8% and 84.9% in evaluating the expressions to match the target values. For the cases inferred incorrectly, the results differed from the targets by only one or two characters. The models notably learned to add, subtract and multiply both positive and negative decimal numbers of variable digits assigned to symbolic variables.

Connected through hyperedge HE-1 (induced by source paper "on identifiability in transformers"):
  with members:
    - "attention is not not explanation"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "attention is not all you need for commonsense reasoning"
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "adding interpretable attention to neural translation models improves word alignment"

Connected through hyperedge HE-2 (induced by source paper "lightweight and efficient neural natural language processing with quaternion networks"):
  with members:
    - "quaternion collaborative filtering for recommendation"
    - "quaternion convolutional neural networks"
    - "universal transformers"
    - "quaternion knowledge graph embedding"
    - "quaternet a quaternion based recurrent model for human motion"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-3 (induced by source paper "token level ensemble distillation for grapheme to phoneme conversion"):
  with members:
    - "fastspeech fast robust and controllable text to speech"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "large scale distributed neural network training through online distillation"

Connected through hyperedge HE-4 (induced by source paper "question generation by transformers"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep reinforcement learning for sequence to sequence models"
    - "improving neural question generation using answer separation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-5 (induced by source paper "multi label product categorization using multi modal fusion models"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "image based recommendations on styles and substitutes"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

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)