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: "condensed memory networks for clinical diagnostic inferencing"
  Abstract: Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab tests etc.). In contrast, we explore the problem using free-text medical notes recorded in an electronic health record (EHR). Complex tasks like these can benefit from structured knowledge bases, but those are not scalable. We instead exploit raw text from Wikipedia as a knowledge source. Memory networks have been demonstrated to be effective in tasks which require comprehension of free-form text. They use the final iteration of the learned representation to predict probable classes. We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory. Experiments on the MIMIC-III dataset show that the proposed model outperforms other variants of memory networks to predict the most probable diagnoses given a complex clinical scenario.

Connected through hyperedge HE-1 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  with members:
    - "exploring dimensionality reductions with forward and backward projections"
    - "brain tumor segmentation with deep neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "direct manipulation visualization of deep networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "icd coding from clinical text using multi filter residual convolutional neural network"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "hibert document level pre training of hierarchical bidirectional transformers for document summarization"
    - "explainable prediction of medical codes from clinical text"
    - "multimodal machine learning for automated icd coding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "towards automated icd coding using deep learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "explicit blurred memory network for analyzing patient electronic health records"):
  with members:
    - "attention is not explanation"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "memory augmented neural networks with wormhole connections"
    - "end to end memory networks"
    - "neural turing machines"

Connected through hyperedge HE-4 (induced by source paper "joint embedding of words and labels for text classification"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "explainable prediction of medical codes from clinical text"
    - "topic compositional neural language model"
    - "attention is all you need"
    - "attention over attention neural networks for reading comprehension"
    - "deconvolutional latent variable model for text sequence matching"
    - "convolutional sequence to sequence learning"
    - "deconvolutional paragraph representation learning"

Connected through hyperedge HE-5 (induced by source paper "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "temporal convolutional networks for action segmentation and detection"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "doctor ai predicting clinical events via recurrent neural networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-6 (induced by source paper "dual control memory augmented neural networks for treatment recommendations"):
  with members:
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "visual question answering with memory augmented networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "end to end memory networks"
    - "learning to diagnose with lstm recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural turing machines"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "explainable prediction of medical codes from clinical text"):
  with members:
    - "improving palliative care with deep learning"
    - "towards automated icd coding using deep learning"
    - "language modeling with gated convolutional networks"
    - "rationalizing neural predictions"
    - "a neural attention model for abstractive sentence summarization"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "challenges in the automatic analysis of students diagnostic reasoning"):
  with members:
    - "multi task learning for argumentation mining in low resource settings"
    - "learning word vectors for 157 languages"
    - "argumentation mining in user generated web discourse"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "learning recursive segments for discourse parsing"

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)