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: "structural attention neural networks for improved sentiment analysis"
  Abstract: We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottom-up and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree. To our knowledge, the proposed models achieve state of the art performance on the Stanford Sentiment Treebank dataset.

Connected through hyperedge HE-1 (induced by source paper "a framework for end to end learning on semantic tree structured data"):
  with members:
    - "graph neural networks a review of methods and applications"
    - "deep sets"
    - "learning to play general video games via an object embedding network"
    - "tree to tree neural networks for program translation"
    - "approximation capability of neural networks on spaces of probability measures and tree structured domains"
    - "abstract syntax networks for code generation and semantic parsing"
    - "proximal policy optimization algorithms"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-2 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "towards robust neural machine translation"
    - "deep contextualized word representations"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"
    - "modeling past and future for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "towards lossless encoding of sentences"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "an efficient model for sentiment analysis of electronic product reviews in vietnamese"):
  with members:
    - "attention is all you need"
    - "frustratingly short attention spans in neural language modeling"
    - "squeeze and excitation networks"
    - "convolutional sequence to sequence learning"
    - "long short term memory networks for machine reading"
    - "enriching word vectors with subword information"
    - "hierarchical question image co attention for visual question answering"
    - "recurrent models of visual attention"

Connected through hyperedge HE-5 (induced by source paper "bi directional block self attention for fast and memory efficient sequence modeling"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "reinforced mnemonic reader for machine comprehension"
    - "structured attention networks"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "ruminating reader reasoning with gated multi hop attention"
    - "more is less a more complicated network with less inference complexity"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"

Connected through hyperedge HE-6 (induced by source paper "self attention a better building block for sentiment analysis neural network classifiers"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "frustratingly short attention spans in neural language modeling"
    - "convolutional sequence to sequence learning"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "hierarchical question image co attention for visual question answering"
    - "improved semantic representations from tree structured long short term memory networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "tag enhanced tree structured neural networks for implicit discourse relation classification"):
  with members:
    - "textual entailment with structured attentions and composition"
    - "adversarial connective exploiting networks for implicit discourse relation classification"
    - "tree to sequence attentional neural machine translation"
    - "enhanced lstm for natural language inference"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"

Connected through hyperedge HE-8 (induced by source paper "syntactic dependency representations in neural relation classification"):
  with members:
    - "sirius ltg uio at semeval 2018 task 7 convolutional neural networks with shortest dependency paths for semantic relation extraction and classification in scientific papers"
    - "a simple and accurate syntax agnostic neural model for dependency based semantic role labeling"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

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)