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: "dragnn a transition based framework for dynamically connected neural networks"
  Abstract: In this work, we present a compact, modular framework for constructing new recurrent neural architectures. Our basic module is a new generic unit, the Transition Based Recurrent Unit (TBRU). In addition to hidden layer activations, TBRUs have discrete state dynamics that allow network connections to be built dynamically as a function of intermediate activations. By connecting multiple TBRUs, we can extend and combine commonly used architectures such as sequence-to-sequence, attention mechanisms, and recursive tree-structured models. A TBRU can also serve as both an {\em encoder} for downstream tasks and as a {\em decoder} for its own task simultaneously, resulting in more accurate multi-task learning. We call our approach Dynamic Recurrent Acyclic Graphical Neural Networks, or DRAGNN. We show that DRAGNN is significantly more accurate and efficient than seq2seq with attention for syntactic dependency parsing and yields more accurate multi-task learning for extractive summarization tasks.

Connected through hyperedge HE-1 (induced by source paper "sentence encoding with tree constrained relation networks"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "structured attention networks"
    - "learning structured text representations"
    - "attention is all you need"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"
    - "neural probabilistic model for non projective mst parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "attentive tensor product learning for language generation and grammar parsing"):
  with members:
    - "attention is all you need"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "semantic compositional networks for visual captioning"
    - "learning to compose words into sentences with reinforcement learning"
    - "deep residual learning for image recognition"
    - "improved semantic representations from tree structured long short term memory networks"
    - "neural module networks"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-3 (induced by source paper "tensor product generation networks for deep nlp modeling"):
  with members:
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "question answering with grammatically interpretable representations"
    - "semantic compositional networks for visual captioning"
    - "learning to compose words into sentences with reinforcement learning"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "language models for image captioning the quirks and what works"
    - "deep residual learning for image recognition"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-4 (induced by source paper "syntaxnet models for the conll 2017 shared task"):
  with members:
    - "exploring the limits of language modeling"
    - "hierarchical multiscale recurrent neural networks"
    - "multilingual language processing from bytes"
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "transition based dependency parsing with stack long short term memory"

Connected through hyperedge HE-5 (induced by source paper "sling a framework for frame semantic parsing"):
  with members:
    - "efficient estimation of word representations in vector space"

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)