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 networks with external memory for language understanding"
  Abstract: Recurrent Neural Networks (RNNs) have become increasingly popular for the task of language understanding. In this task, a semantic tagger is deployed to associate a semantic label to each word in an input sequence. The success of RNN may be attributed to its ability to memorize long-term dependence that relates the current-time semantic label prediction to the observations many time instances away. However, the memory capacity of simple RNNs is limited because of the gradient vanishing and exploding problem. We propose to use an external memory to improve memorization capability of RNNs. We conducted experiments on the ATIS dataset, and observed that the proposed model was able to achieve the state-of-the-art results. We compare our proposed model with alternative models and report analysis results that may provide insights for future research.

Connected through hyperedge HE-1 (induced by source paper "improving slot filling by utilizing contextual information"):
  with members:
    - "bert for joint intent classification and slot filling"
    - "gcdt a global context enhanced deep transition architecture for sequence labeling"
    - "pytext a seamless path from nlp research to production"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "rasa open source language understanding and dialogue management"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"

Connected through hyperedge HE-2 (induced by source paper "character level feature extraction with densely connected networks"):
  with members:
    - "neural models for sequence chunking"
    - "don t decay the learning rate increase the batch size"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"

Connected through hyperedge HE-3 (induced by source paper "a new concept of deep reinforcement learning based augmented general sequence tagging system"):
  with members:
    - "deep contextualized word representations"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "user information augmented semantic frame parsing using coarse to fine neural networks"):
  with members:
    - "progressive neural networks"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "context aware computing for the internet of things a survey"

Connected through hyperedge HE-5 (induced by source paper "a bi model based rnn semantic frame parsing model for intent detection and slot filling"):
  with members:
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "visual question answering with memory augmented networks"):
  with members:
    - "learning to remember rare events"
    - "pointing the unknown words"
    - "training recurrent answering units with joint loss minimization for vqa"
    - "multimodal residual learning for visual qa"
    - "hierarchical question image co attention for visual question answering"
    - "the vqa machine learning how to use existing vision algorithms to answer new questions"
    - "dynamic memory networks for visual and textual question answering"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

Connected through hyperedge HE-7 (induced by source paper "neural models for sequence chunking"):
  with members:
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for natural language understanding"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"

Connected through hyperedge HE-8 (induced by source paper "concept transfer learning for adaptive language understanding"):
  with members:
    - "neural models for sequence chunking"
    - "sequential convolutional neural networks for slot filling in spoken language understanding"
    - "leveraging sentence level information with encoder lstm for semantic slot filling"
    - "encoder decoder with focus mechanism for sequence labelling based spoken language understanding"
    - "attention based recurrent neural network models for joint intent detection and slot filling"
    - "neural machine translation by jointly learning to align and translate"

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)