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: "capturing long range contextual dependencies with memory enhanced conditional random fields"
  Abstract: Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items. Attempts to extend CRFs to capture long-range dependencies have largely come at the cost of computational complexity and approximate inference. In this work, we propose an extension to CRFs by integrating external memory, taking inspiration from memory networks, thereby allowing CRFs to incorporate information far beyond neighbouring steps. Experiments across two tasks show substantial improvements over strong CRF and LSTM baselines.

Connected through hyperedge HE-1 (induced by source paper "grn gated relation network to enhance convolutional neural network for named entity recognition"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "hybrid semi markov crf for neural sequence labeling"
    - "deep contextualized word representations"
    - "deep active learning for named entity recognition"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

Connected through hyperedge HE-2 (induced by source paper "evaluating the utility of document embedding vector difference for relation learning"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "learning distributed representations of sentences from unlabelled data"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"

Connected through hyperedge HE-3 (induced by source paper "dialogue act recognition via crf attentive structured network"):
  with members:
    - "smarnet teaching machines to read and comprehend like human"
    - "structured attention networks"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "text understanding with the attention sum reader network"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "a latent variable recurrent neural network for discourse relation language models"

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)