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: "semantic neural machine translation using amr"
  Abstract: It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences corres...

Connected through hyperedge HE-1 (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"
    - "weighted transformer network for machine translation"
    - "tied multitask learning for neural speech translation"
    - "self attention with relative position representations"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"

Connected through hyperedge HE-2 (induced by source paper "automatically extracting challenge sets for non local phenomena neural machine translation"):
  with members:
    - "star transformer"
    - "linguistic evaluation of german english machine translation using a test suite"
    - "linguistic knowledge and transferability of contextual representations"
    - "semantic structural evaluation for text simplification"
    - "do rnns learn human like abstract word order preferences"
    - "a challenge set for french english machine translation"
    - "automatic metric validation for grammatical error correction"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "leveraging dependency forest for neural medical relation extraction"):
  with members:
    - "graph based neural sentence ordering"
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "a silver standard corpus of human phenotype gene relations"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "attention guided graph convolutional networks for relation extraction"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "exploiting rich syntactic information for semantic parsing with graph to sequence model"

Connected through hyperedge HE-4 (induced by source paper "self attention with structural position representations"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "exploiting sentential context for neural machine translation"
    - "towards better modeling hierarchical structure for self attention with ordered neurons"
    - "modeling recurrence for transformer"
    - "adaptive attention span in transformers"
    - "exploiting deep representations for neural machine translation"
    - "self attention with relative position representations"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-5 (induced by source paper "iterative dual domain adaptation for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "non parametric adaptation for neural machine translation"
    - "improving domain adaptation translation with domain invariant and specific information"
    - "a survey of domain adaptation for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "multi domain neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "graph based neural sentence ordering"):
  with members:
    - "graph to sequence learning using gated graph neural networks"
    - "n ary relation extraction using graph state lstm"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "a graph to sequence model for amr to text generation"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "enhancing amr to text generation with dual graph representations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "densely connected graph convolutional networks for graph to sequence learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "neural collective entity linking based on recurrent random walk network learning"):
  with members:
    - "graph based neural sentence ordering"
    - "neural collective entity linking"
    - "end to end neural entity linking"
    - "improving entity linking by modeling latent relations between mentions"
    - "sentence state lstm for text representation"
    - "attention is all you need"
    - "deep joint entity disambiguation with local neural attention"
    - "capturing semantic similarity for entity linking with convolutional 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.CL(Computation and Language)