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: "recurrent neural network for text classification with multi task learning"
  Abstract: Neural network based methods have obtained great progress on a variety of natural language processing tasks. However, in most previous works, the models are learned based on single-task supervised objectives, which often suffer from insufficient training data. In this paper, we use the multi-task learning framework to jointly learn across multiple related tasks. Based on recurrent neural network, we propose three different mechanisms of sharing information to model text with task-specific and shared layers. The entire network is trained jointly on all these tasks. Experiments on four benchmark text classification tasks show that our proposed models can improve the performance of a task with the help of other related tasks.

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 "short text classification via knowledge powered attention with similarity matrix based cnn"):
  with members:
    - "deep short text classification with knowledge powered attention"
    - "cn probase a data driven approach for large scale chinese taxonomy construction"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"
    - "differentiating concepts and instances for knowledge graph embedding"
    - "question answering over freebase via attentive rnn with similarity matrix based cnn"
    - "multilingual hierarchical attention networks for document classification"
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-3 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee"):
  with members:
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "on the convergence rate of training recurrent neural networks"
    - "a local regret in nonconvex online learning"
    - "learning with random learning rates"
    - "stable recurrent models"
    - "lstm a search space odyssey"
    - "efficient regret minimization in non convex games"
    - "training deep networks without learning rates through coin betting"

Connected through hyperedge HE-4 (induced by source paper "label guided learning for text classification"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "relational inductive biases deep learning and graph networks"
    - "joint embedding of words and labels for text classification"
    - "gile a generalized input label embedding for text classification"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "semi supervised classification with graph convolutional networks"
    - "bag of tricks for efficient text classification"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-5 (induced by source paper "an investigation of interpretability techniques for deep learning in predictive process analytics"):
  with members:
    - "complication risk profiling in diabetes care a bayesian multi task and feature relationship learning approach"
    - "consistent individualized feature attribution for tree ensembles"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "interpreting cnn knowledge via an explanatory graph"
    - "a unified approach to interpreting model predictions"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-6 (induced by source paper "on identifying hashtags in disaster twitter data"):
  with members:
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "modeling noisiness to recognize named entities using multitask neural networks on social media"
    - "deep contextualized word representations"
    - "domain adaptation with adversarial training and graph embeddings"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "character level convolutional networks for text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-7 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "searching for effective neural extractive summarization what works and what s next"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "content selection in deep learning models of summarization"
    - "bottom up abstractive summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "deep contextualized word representations"
    - "ranking sentences for extractive summarization with reinforcement learning"

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)