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: "the effect of heterogeneous data for alzheimer s disease detection from speech"
  Abstract: Speech datasets for identifying Alzheimer's disease (AD) are generally restricted to participants performing a single task, e.g. describing an image shown to them. As a result, models trained on linguistic features derived from such datasets may not be generalizable across tasks. Building on prior work demonstrating that same-task data of healthy participants helps improve AD detection on a single-task dataset of pathological speech, we augment an AD-specific dataset consisting of subjects describing a picture with multi-task healthy data. We demonstrate that normative data from multiple speech-based tasks helps improve AD detection by up to 9%. Visualization of decision boundaries reveals that models trained on a combination of structured picture descriptions and unstructured conversational speech have the least out-of-task error and show the most potential to generalize to multiple tasks. We analyze the impact of age of the added samples and if they affect fairness in classification. We also provide explanations for a possible inductive bias effect across tasks using model-agnostic feature anchors. This work highlights the need for heterogeneous datasets for encoding changes in multiple facets of cognition and for developing a task-independent AD detection model.

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"
    - "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 "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"
    - "on the importance of normative data in speech based assessment"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-3 (induced by source paper "a multi modal feature embedding approach to diagnose alzheimer disease from spoken language"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "isolating effects of age with fair representation learning when assessing dementia"
    - "end to end text dependent speaker verification"
    - "parallel training of dnns with natural gradient and parameter averaging"

Connected through hyperedge HE-4 (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:
    - "detecting cognitive impairments by agreeing on interpretations of linguistic features"
    - "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.LG(Machine Learning)