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: "contextual lstm clstm models for large scale nlp tasks"
  Abstract: Documents exhibit sequential structure at multiple levels of abstraction (e.g., sentences, paragraphs, sections). These abstractions constitute a natural hierarchy for representing the context in which to infer the meaning of words and larger fragments of text. In this paper, we present CLSTM (Contextual LSTM), an extension of the recurrent neural network LSTM (Long-Short Term Memory) model, where we incorporate contextual features (e.g., topics) into the model. We evaluate CLSTM on three specific NLP tasks: word prediction, next sentence selection, and sentence topic prediction. Results from experiments run on two corpora, English documents in Wikipedia and a subset of articles from a recent snapshot of English Google News, indicate that using both words and topics as features improves performance of the CLSTM models over baseline LSTM models for these tasks. For example on the next sentence selection task, we get relative accuracy improvements of 21% for the Wikipedia dataset and 18% for the Google News dataset. This clearly demonstrates the significant benefit of using context appropriately in natural language (NL) tasks. This has implications for a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems.

Connected through hyperedge HE-1 (induced by source paper "connecting gans and mfgs"):
  with members:
    - "smoothness and stability in gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic segmentation using adversarial networks"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "image to language understanding captioning approach"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "using sentence level lstm language models for script inference"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "deep residual learning for image recognition"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "rethinking the inception architecture for computer vision"
    - "from captions to visual concepts and back"

Connected through hyperedge HE-3 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks"):
  with members:
    - "fine tune bert for extractive summarization"
    - "jointly extracting and compressing documents with summary state representations"
    - "scisummnet a large annotated corpus and content impact models for scientific paper summarization with citation networks"
    - "mass masked sequence to sequence pre training for language generation"
    - "generating summaries with topic templates and structured convolutional decoders"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "unified language model pre training for natural language understanding and generation"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-4 (induced by source paper "approximating interactive human evaluation with self play for open domain dialog systems"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "unifying human and statistical evaluation for natural language generation"
    - "hierarchical reinforcement learning for open domain dialog"
    - "training millions of personalized dialogue agents"
    - "towards empathetic open domain conversation models a new benchmark and dataset"
    - "a hierarchical latent structure for variational conversation modeling"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "mojitalk generating emotional responses at scale"

Connected through hyperedge HE-5 (induced by source paper "conclusion supplement answer generation for non factoid questions"):
  with members:
    - "document expansion by query prediction"
    - "multi passage machine reading comprehension with cross passage answer verification"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "stochastic answer networks for natural language inference"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "attention is all you need"
    - "s net from answer extraction to answer generation for machine reading comprehension"
    - "deep reinforcement learning for dialogue generation"

Connected through hyperedge HE-6 (induced by source paper "tacam topic and context aware argument mining"):
  with members:
    - "robust argument unit recognition and classification"
    - "deep contextualized word representations"
    - "cross topic argument mining from heterogeneous sources using attention based neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "what is the essence of a claim cross domain claim identification"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "instance based transfer learning for multilingual deep retrieval"):
  with members:
    - "what comes next extractive summarization by next sentence prediction"
    - "gromov wasserstein alignment of word embedding spaces"
    - "stochastic negative mining for learning with large output spaces"
    - "distant supervision from disparate sources for low resource part of speech tagging"
    - "unsupervised multilingual word embeddings"
    - "know what you don t know unanswerable questions for squad"
    - "fine tuned language models for text classification"
    - "on the limitations of unsupervised bilingual dictionary induction"

Connected through hyperedge HE-8 (induced by source paper "estimating early fundraising performance of innovations via graph based market environment model"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "context aware visual compatibility prediction"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)