human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multi channel reverse dictionary model"):
  Abstract: A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on this https URL.

Includes members:
  Member "enhancing recurrent neural networks with sememes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets")
  Member "modeling semantic compositionality with sememe knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets")
  Member "open the boxes of words incorporating sememes into textual adversarial attack":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets")
  Member "openhownet an open sememe based lexical knowledge base":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards building a multilingual sememe knowledge base predicting sememes for babelnet synsets")
  Member "using multi sense vector embeddings for reverse dictionaries":
    also connected through other hyperedges:
      (none)
  Member "incorporating glosses into neural word sense disambiguation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lexical sememe prediction using dictionary definitions by capturing local semantic correspondence")
  Member "building a knowledge graph from natural language definitions for interpretable text entailment recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identifying and explaining discriminative attributes")
  Member "learning to compute word embeddings on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")

Question: Please tell me which class the source paper 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)