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: "on the importance of normative data in speech based assessment"
  Abstract: Data sets for identifying Alzheimer's disease (AD) are often relatively sparse, which limits their ability to train generalizable models. Here, we augment such a data set, DementiaBank, with each of two normative data sets, the Wisconsin Longitudinal Study and Talk2Me, each of which employs a speech-based picture-description assessment. Through minority class oversampling with ADASYN, we outperform state-of-the-art results in binary classification of people with and without AD in DementiaBank. This work highlights the effectiveness of combining sparse and difficult-to-acquire patient data with relatively large and easily accessible normative datasets.

Connected through hyperedge HE-1 (induced by source paper "variations on the chebyshev lagrange activation function"):
  with members:
    - "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "polynomial regression as an alternative to neural nets"
    - "semi supervised classification by reaching consensus among modalities"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "deepconsensus using the consensus of features from multiple layers to attain robust image classification"
    - "shake shake regularization"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "lexical features are more vulnerable syntactic features have more predictive power"):
  with members:
    - "evaluating the state of the art of end to end natural language generation the e2e nlg challenge"
    - "impact of asr on alzheimer s disease detection all errors are equal but deletions are more equal than others"
    - "isolating effects of age with fair representation learning when assessing dementia"
    - "controlling personality based stylistic variation with neural natural language generators"
    - "detecting cognitive impairments by agreeing on interpretations of linguistic features"
    - "generating natural language adversarial examples"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "detecting dementia in mandarin chinese using transfer learning from a parallel corpus"):
  with members:
    - "learning with augmented features for heterogeneous domain adaptation"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "detecting cognitive impairments by agreeing on interpretations of linguistic features"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "one ruler for all languages multi lingual dialogue evaluation with adversarial multi task learning"
    - "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts"
    - "unified deep supervised domain adaptation and generalization"
    - "adversarial multi task learning for text classification"
    - "cross language learning with adversarial neural networks application to community question answering"
    - "unsupervised cross domain image generation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "the effect of heterogeneous data for alzheimer s disease detection from speech"):
  with members:
    - "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
    - "an overview of multi task learning in deep neural networks"
    - "a model of inductive bias learning"

Connected through hyperedge HE-6 (induced by source paper "augmenting word2vec with latent dirichlet allocation within a clinical application"):
  with members:
    - "distributed representations of words and phrases and their compositionality"

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)