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: "a fixed size encoding method for variable length sequences with its application to neural network language models"
  Abstract: In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNN-LMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular RNN-LMs.

Connected through hyperedge HE-1 (induced by source paper "a multi task learning approach for named entity recognition using local detection"):
  with members:
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "neural networks models for entity discovery and linking"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-2 (induced by source paper "effective context and fragment feature usage for named entity recognition"):
  with members:
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "multi task cross lingual sequence tagging from scratch"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "a survey on deep learning for named entity recognition"):
  with members:
    - "learning a unified named entity tagger from multiple partially annotated corpora for efficient adaptation"
    - "a unified mrc framework for named entity recognition"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "a little annotation does a lot of good a study in bootstrapping low resource named entity recognizers"
    - "dependency guided lstm crf for named entity recognition"
    - "hierarchical contextualized representation for named entity recognition"
    - "dice loss for data imbalanced nlp tasks"
    - "attending to entities for better text understanding"

Connected through hyperedge HE-4 (induced by source paper "using the output embedding to improve language models"):
  with members:
    - "noisy activation functions"
    - "gated word character recurrent language model"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "a dual embedding space model for document ranking"
    - "recurrent highway networks"
    - "neural architecture search with reinforcement learning"
    - "edinburgh neural machine translation systems for wmt 16"
    - "achieving human parity in conversational speech recognition"

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.NE(Neural and Evolutionary Computing)