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: "exploiting semantics in neural machine translation with graph convolutional networks"
  Abstract: Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. In this work, we are the first to incorporate information about predicate-argument structure of source sentences (namely, semantic-role representations) into neural machine translation. We use Graph Convolutional Networks (GCNs) to inject a semantic bias into sentence encoders and achieve improvements in BLEU scores over the linguistic-agnostic and syntax-aware versions on the English--German language pair.

Connected through hyperedge HE-1 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "recurrent graph syntax encoder for neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "semantic neural machine translation using amr"
    - "weighted transformer network for machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"

Connected through hyperedge HE-3 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs"):
  with members:
    - "coarse grain fine grain coattention network for multi evidence question answering"
    - "global to local memory pointer networks for task oriented dialogue"
    - "densely connected attention propagation for reading comprehension"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "neural models for reasoning over multiple mentions using coreference"
    - "graph convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "continuous graph flow for flexible density estimation"):
  with members:
    - "layoutvae stochastic scene layout generation from a label set"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph to sequence learning using gated graph neural networks"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"
    - "image generation from layout"

Connected through hyperedge HE-5 (induced by source paper "semantic role labeling with iterative structure refinement"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "second order semantic dependency parsing with end to end neural networks"
    - "capturing argument interaction in semantic role labeling with capsule networks"
    - "benchmarking approximate inference methods for neural structured prediction"
    - "achieving human parity on automatic chinese to english news translation"
    - "polyglot semantic role labeling"
    - "image segmentation by iterative inference from conditional score estimation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-6 (induced by source paper "neural language priors"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "improving tree lstm with tree attention"
    - "no training required exploring random encoders for sentence classification"
    - "universal transformers"
    - "weighted transformer network for machine translation"
    - "self attention with relative position representations"
    - "universal sentence encoder"
    - "dynamic compositionality in recursive neural networks with structure aware tag representations"

Connected through hyperedge HE-7 (induced by source paper "self attentional models for lattice inputs"):
  with members:
    - "hybrid self attention network for machine translation"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for named entity recognition"
    - "attention is all you need"
    - "on the fly operation batching in dynamic computation graphs"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-8 (induced by source paper "translate and label an encoder decoder approach for cross lingual semantic role labeling"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "polyglot semantic role labeling"
    - "a span selection model for semantic role labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)