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: "densely connected graph convolutional networks for graph to sequence learning"
  Abstract: We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph convolutional networks (GCNs). Unlike various existing approaches where shallow architectures were used for capturing local structural information only, we introduce a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Networks (DCGCNs). Such a deep architecture is able to integrate both local and non-local features to learn a better structural representation of a graph. Our model outperforms the state-of-the-art neural models significantly on AMRto-text generation and syntax-based neural machine translation.

Connected through hyperedge HE-1 (induced by source paper "depth adaptive graph recurrent network for text classification"):
  with members:
    - "graph based neural sentence ordering"
    - "depth adaptive transformer"
    - "r transformer recurrent neural network enhanced transformer"
    - "learning to remember more with less memorization"
    - "cm net a novel collaborative memory network for spoken language understanding"
    - "universal transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "graph transformer for graph to sequence learning"
    - "structural neural encoders for amr to text generation"
    - "neural data to text generation a comparison between pipeline and end to end architectures"
    - "enhancing amr to text generation with dual graph representations"
    - "step by step separating planning from realization in neural data to text generation"
    - "modeling graph structure in transformer for better amr to text generation"

Connected through hyperedge HE-3 (induced by source paper "attention guided graph convolutional networks for relation extraction"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "representation learning on graphs with jumping knowledge networks"
    - "n ary relation extraction using graph state lstm"
    - "cross sentence n ary relation extraction with graph lstms"
    - "end to end neural coreference resolution"
    - "improved neural relation detection for knowledge base question answering"

Connected through hyperedge HE-4 (induced by source paper "graph transformer for graph to sequence learning"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "structural neural encoders for amr to text generation"
    - "factorising amr generation through syntax"
    - "toward abstractive summarization using semantic representations"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "a graph to sequence model for amr to text generation"

Connected through hyperedge HE-5 (induced by source paper "selective attention based graph convolutional networks for aspect level sentiment classification"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "targeted sentiment classification with attentional encoder network"
    - "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "attention guided graph convolutional networks for relation extraction"
    - "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification"
    - "exploiting coarse to fine task transfer for aspect level sentiment classification"
    - "graph convolution over pruned dependency trees improves relation extraction"

Connected through hyperedge HE-6 (induced by source paper "modeling graph structure in transformer for better amr to text generation"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "structural neural encoders for amr to text generation"
    - "factorising amr generation through syntax"
    - "self attention with relative position representations"
    - "graph to sequence learning using gated graph neural networks"
    - "a graph to sequence model for amr to text generation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "aligning cross lingual entities with multi aspect information"):
  with members:
    - "matching entities across different knowledge graphs with graph embeddings"
    - "simple attention based representation learning for ranking short social media posts"
    - "attention guided graph convolutional networks for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "machine translation using semantic web technologies a survey"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"

Connected through hyperedge HE-8 (induced by source paper "enhancing amr to text generation with dual graph representations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "semantic neural machine translation using amr"
    - "factorising amr generation through syntax"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a graph to sequence model for amr to text generation"
    - "how powerful are graph neural networks"
    - "abstract meaning representation for multi document summarization"
    - "deep graph convolutional encoders for structured data to text generation"

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)