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 "a sequential neural encoder with latent structured description for modeling sentences"):
  Abstract: In this paper, we propose a sequential neural encoder with latent structured description (SNELSD) for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory (LSTM) units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.

Includes members:
  Member "tensor fusion network for multimodal sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fgn fusion glyph network for chinese named entity recognition")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "enhancing and combining sequential and tree lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zero shot cross lingual transfer with meta learning")
  Member "a decomposable attention model for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "enhanced lstm for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial filters of dataset biases")
  Member "simple and accurate dependency parsing using bidirectional lstm feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parsing as pretraining")
  Member "neural architectures for named entity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "knowledge graphs")
  Member "neural tree indexers for text understanding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ordered memory")

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)