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: "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
  Abstract: Alzheimer's disease (AD) is an irreversible and progressive brain disease that can be stopped or slowed down with medical treatment. Language changes serve as a sign that a patient's cognitive functions have been impacted, potentially leading to early diagnosis. In this work, we use NLP techniques to classify and analyze the linguistic characteristics of AD patients using the DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs, and their combination, to distinguish between language samples from AD and control patients. We achieve a new independent benchmark accuracy for the AD classification task. More importantly, we next interpret what these neural models have learned about the linguistic characteristics of AD patients, via analysis based on activation clustering and first-derivative saliency techniques. We then perform novel automatic pattern discovery inside activation clusters, and consolidate AD patients' distinctive grammar patterns. Additionally, we show that first derivative saliency can not only rediscover previous language patterns of AD patients, but also shed light on the limitations of neural models. Lastly, we also include analysis of gender-separated AD data.

Connected through hyperedge HE-1 (induced by source paper "variations on the chebyshev lagrange activation function"):
  with members:
    - "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"
    - "on the importance of normative data in speech based assessment"
    - "shake shake regularization"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "enriching neural models with targeted features for dementia detection"):
  with members:
    - "ai meets austen towards human robot discussions of literary metaphor"
    - "a c lstm neural network for text classification"

Connected through hyperedge HE-3 (induced by source paper "detecting alzheimer s disease by estimating attention and elicitation path through the alignment of spoken picture descriptions with the picture prompt"):
  with members:
    - "eye tracking for everyone"

Connected through hyperedge HE-4 (induced by source paper "analyzing and interpreting convolutional neural networks in nlp"):
  with members:
    - "seq2seq vis a visual debugging tool for sequence to sequence models"
    - "convolutional sequence to sequence learning"
    - "interpreting neural networks to improve politeness comprehension"
    - "representation of linguistic form and function in recurrent neural networks"
    - "a convolutional encoder model for neural machine translation"
    - "visualizing and understanding neural models in nlp"
    - "visualizing and understanding recurrent networks"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-5 (induced by source paper "the effect of heterogeneous data for alzheimer s disease detection from speech"):
  with members:
    - "on the importance of normative data in speech based assessment"
    - "an overview of multi task learning in deep neural networks"
    - "a model of inductive bias learning"

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)