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 cognitive impairments by agreeing on interpretations of linguistic features"
  Abstract: Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data could be expensive, and hand-carving features are burdensome. In this paper, we take a third approach, putting forward Consensus Networks (CN), a framework to classify after reaching agreements between modalities. We divide the linguistic features into non-overlapping subsets according to their modalities, let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that empirically improve the performance of CN. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in Consensus Networks, we visualize the interpretation vectors during training procedures. They demonstrate symmetry in an aggregate manner. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.

Connected through hyperedge HE-1 (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"
    - "generating natural language adversarial examples"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "on the importance of normative data in speech based assessment"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-2 (induced by source paper "current challenges in spoken dialogue systems and why they are critical for those living with dementia"):
  with members:
    - "an incremental turn taking model for task oriented dialog systems"
    - "incrementalizing rasa s open source natural language understanding pipeline"
    - "investigating speech features for continuous turn taking prediction using lstms"
    - "wav2letter the fastest open source speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "impact of asr on alzheimer s disease detection all errors are equal but deletions are more equal than others"):
  with members:
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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)