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: "graph2seq graph to sequence learning with attention based neural networks"
  Abstract: Celebrated \emph{Sequence to Sequence learning (Seq2Seq)} and its fruitful variants are powerful models to achieve excellent performance on the tasks that map sequences to sequences. However, these are many machine learning tasks with inputs naturally represented in a form of graphs, which imposes significant challenges to existing Seq2Seq models for lossless conversion from its graph form to the sequence. In this work, we present a general end-to-end approach to map the input graph to a sequence of vectors, and then another attention-based LSTM to decode the target sequence from these vectors. Specifically, to address inevitable information loss for data conversion, we introduce a novel graph-to-sequence neural network model that follows the encoder-decoder architecture. Our method first uses an improved graph-based neural network to generate the node and graph embeddings by a novel aggregation strategy to incorporate the edge direction information into the node embeddings. We also propose an attention based mechanism that aligns node embeddings and decoding sequence to better cope with large graphs. Experimental results on bAbI task, Shortest Path Task, and Natural Language Generation Task demonstrate that our model achieves the state-of-the-art performance and significantly outperforms other baselines. We also show that with the proposed aggregation strategy, our proposed model is able to quickly converge to good performance.

Connected through hyperedge HE-1 (induced by source paper "towards graph representation learning in emergent communication"):
  with members:
    - "capacity bandwidth and compositionality in emergent language learning"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "egg a toolkit for research on emergence of language in games"
    - "pytorch an imperative style high performance deep learning library"
    - "how agents see things on visual representations in an emergent language game"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "deep iterative and adaptive learning for graph neural networks"
    - "unified language model pre training for natural language understanding and generation"
    - "sql to text generation with graph to sequence model"
    - "towards a better metric for evaluating question generation systems"

Connected through hyperedge HE-3 (induced by source paper "natural question generation with reinforcement learning based graph to sequence model"):
  with members:
    - "bidirectional attentive memory networks for question answering over knowledge bases"
    - "learning to generate questions by learningwhat not to generate"
    - "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "sql to text generation with graph to sequence model"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "improving neural question generation using answer separation"

Connected through hyperedge HE-4 (induced by source paper "using local knowledge graph construction to scale seq2seq models to multi document inputs"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multi news a large scale multi document summarization dataset and abstractive hierarchical model"
    - "transformer xl attentive language models beyond a fixed length context"
    - "lattice cnns for matching based chinese question answering"
    - "adaptive attention span in transformers"
    - "eli5 long form question answering"
    - "graph neural networks a review of methods and applications"
    - "building dynamic knowledge graphs from text using machine reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "learning to fix build errors with graph2diff neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "neural program repair by jointly learning to localize and repair"
    - "getafix learning to fix bugs automatically"
    - "a comprehensive survey on graph neural networks"
    - "generative code modeling with graphs"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "graphflow exploiting conversation flow with graph neural networks for conversational machine comprehension"):
  with members:
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "sql to text generation with graph to sequence model"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "contextualized non local neural networks for sequence learning"
    - "coqa a conversational question answering challenge"
    - "open domain question answering using early fusion of knowledge bases and text"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning based text style transfer without parallel training corpus"):
  with members:
    - "improving reinforcement learning based image captioning with natural language prior"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"
    - "a study of reinforcement learning for neural machine translation"
    - "style transfer through back translation"
    - "shaped shared private encoder decoder for text style adaptation"
    - "polite dialogue generation without parallel data"
    - "word mover s embedding from word2vec to document embedding"

Connected through hyperedge HE-8 (induced by source paper "improving attention mechanism in graph neural networks via cardinality preservation"):
  with members:
    - "understanding attention and generalization in graph neural networks"
    - "self attention graph pooling"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"

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.AI(Artificial Intelligence)