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: "sememe prediction learning semantic knowledge from unstructured textual wiki descriptions"
  Abstract: Huge numbers of new words emerge every day, leading to a great need for representing them with semantic meaning that is understandable to NLP systems. Sememes are defined as the minimum semantic units of human languages, the combination of which can represent the meaning of a word. Manual construction of sememe based knowledge bases is time-consuming and labor-intensive. Fortunately, communities are devoted to composing the descriptions of words in the wiki websites. In this paper, we explore to automatically predict lexical sememes based on the descriptions of the words in the wiki websites. We view this problem as a weakly ordered multi-label task and propose a Label Distributed seq2seq model (LD-seq2seq) with a novel soft loss function to solve the problem. In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation. The results show that our LD-seq2seq model not only beats all the baselines significantly on the test set, but also outperforms amateur human annotators in a random subset of the test set.

Connected through hyperedge HE-1 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence"):
  with members:
    - "incorporating glosses into neural word sense disambiguation"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "implementing a reverse dictionary based on word definitions using a node graph architecture"
    - "neural machine translation of rare words with subword units"
    - "learning to understand phrases by embedding the dictionary"
    - "adam a method for stochastic optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "modeling semantic compositionality with sememe knowledge"):
  with members:
    - "cos960 a chinese word similarity dataset of 960 word pairs"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "improving sparse word representations with distributional inference for semantic composition"
    - "aligning packed dependency trees a theory of composition for distributional semantics"
    - "multi step regression learning for compositional distributional semantics"
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-3 (induced by source paper "openhownet an open sememe based lexical knowledge base"):
  with members:
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "semantic unit based dilated convolution for multi label text classification"):
  with members:
    - "global encoding for abstractive summarization"
    - "understanding convolution for semantic segmentation"
    - "convolutional sequence to sequence learning"
    - "neural machine translation in linear time"
    - "wavenet a generative model for raw audio"
    - "end to end memory networks"
    - "effective approaches to attention based neural machine translation"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-5 (induced by source paper "a deep reinforced sequence to set model for multi label text classification"):
  with members:
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "a deep reinforced model for abstractive summarization"
    - "deep reinforcement learning for dialogue generation"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "self critical sequence training for image captioning"
    - "sequence level training with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "review driven multi label music style classification by exploiting style correlations"):
  with members:
    - "exploration on generating traditional chinese medicine prescription from symptoms with an end to end method"
    - "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"
    - "multi label music genre classification from audio text and images using deep features"
    - "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 "evaluating semantic rationality of a sentence a sememe word matching neural network based on hownet"):
  with members:
    - "improving word vector with prior knowledge in semantic dictionary"
    - "a semantic relevance based neural network for text summarization and text simplification"
    - "improving semantic relevance for sequence to sequence learning of chinese social media text summarization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)