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: "recurrent neural network models for disease name recognition using domain invariant features"
  Abstract: Hand-crafted features based on linguistic and domain-knowledge play crucial role in determining the performance of disease name recognition systems. Such methods are further limited by the scope of these features or in other words, their ability to cover the contexts or word dependencies within a sentence. In this work, we focus on reducing such dependencies and propose a domain-invariant framework for the disease name recognition task. In particular, we propose various end-to-end recurrent neural network (RNN) models for the tasks of disease name recognition and their classification into four pre-defined categories. We also utilize convolution neural network (CNN) in cascade of RNN to get character-based embedded features and employ it with word-embedded features in our model. We compare our models with the state-of-the-art results for the two tasks on NCBI disease dataset. Our results for the disease mention recognition task indicate that state-of-the-art performance can be obtained without relying on feature engineering. Further the proposed models obtained improved performance on the classification task of disease names.

Connected through hyperedge HE-1 (induced by source paper "collabonet collaboration of deep neural networks for biomedical named entity recognition"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "neural architectures for named entity recognition"
    - "character aware neural language models"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-2 (induced by source paper "integrating dictionary feature into a deep learning model for disease named entity recognition"):
  with members:
    - "improving multi word entity recognition for biomedical texts"
    - "end to end memory networks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "on the difficulty of training recurrent neural networks"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-3 (induced by source paper "a neural multi task learning framework to jointly model medical named entity recognition and normalization"):
  with members:
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "an overview of multi task learning in deep neural networks"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"
    - "named entity recognition with bidirectional lstm cnns"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "multi task sequence to sequence learning"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "learning named entity tagger using domain specific dictionary"):
  with members:
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "automated phrase mining from massive text corpora"
    - "unsupervised aspect term extraction with b lstm crf using automatically labelled datasets"
    - "swellshark a generative model for biomedical named entity recognition without labeled data"
    - "empower sequence labeling with task aware neural language model"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "unified neural architecture for drug disease and clinical entity recognition"):
  with members:
    - "neural architectures for named entity recognition"
    - "classifying relations by ranking with convolutional neural networks"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "advances in optimizing recurrent networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-6 (induced by source paper "swellshark a generative model for biomedical named entity recognition without labeled data"):
  with members:
    - "neural architectures for named entity 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.CL(Computation and Language)