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: "enhancing recurrent neural networks with sememes"
  Abstract: Sememes, the minimum semantic units of human languages, have been successfully utilized in various natural language processing applications. However, most existing studies exploit sememes in specific tasks and few efforts are made to utilize sememes more fundamentally. In this paper, we propose to incorporate sememes into recurrent neural networks (RNNs) to improve their sequence modeling ability, which is beneficial to all kinds of downstream tasks. We design three different sememe incorporation methods and employ them in typical RNNs including LSTM, GRU and their bidirectional variants. For evaluation, we use several benchmark datasets involving PTB and WikiText-2 for language modeling, SNLI for natural language inference. Experimental results show evident and consistent improvement of our sememe-incorporated models compared with vanilla RNNs, which proves the effectiveness of our sememe incorporation methods. Moreover, we find the sememe-incorporated models have great robustness and outperform adversarial training in defending adversarial attack. All the code and data of this work will be made available to the public.

Connected through hyperedge HE-1 (induced by source paper "multi channel reverse dictionary model"):
  with members:
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "using multi sense vector embeddings for reverse dictionaries"
    - "incorporating glosses into neural word sense disambiguation"
    - "building a knowledge graph from natural language definitions for interpretable text entailment recognition"
    - "learning to compute word embeddings on the fly"
    - "mapping text to knowledge graph entities using multi sense lstms"

Connected through hyperedge HE-2 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets"):
  with members:
    - "multi channel reverse dictionary model"
    - "modeling semantic compositionality with sememe knowledge"
    - "open the boxes of words incorporating sememes into textual adversarial attack"
    - "openhownet an open sememe based lexical knowledge base"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "a novel embedding model for knowledge base completion based on convolutional neural network"
    - "convolutional 2d knowledge graph embeddings"

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)