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: "multi task learning for mental health using social media text"
  Abstract: We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multi-task learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.

Connected through hyperedge HE-1 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-2 (induced by source paper "deep representation learning for clustering of health tweets"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "improving topic models with latent feature word representations"
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character based neural embeddings for tweet clustering"
    - "tweet2vec character based distributed representations for social media"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "adapting deep learning methods for mental health prediction on social media"):
  with members:
    - "not just depressed bipolar disorder prediction on reddit"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a short review of ethical challenges in clinical natural language processing"
    - "detecting and explaining crisis"
    - "depression and self harm risk assessment in online forums"
    - "character level and multi channel convolutional neural networks for large scale authorship attribution"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "suicidal ideation detection a review of machine learning methods and applications"):
  with members:
    - "knowledge transferring via model aggregation for online social care"
    - "decentralized learning with average difference aggregation for proactive online social care"
    - "depression and self harm risk assessment in online forums"
    - "detecting suicidal ideation in chinese microblogs with psychological lexicons"
    - "suicide ideation of individuals in online social networks"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "a computational linguistic study of personal recovery in bipolar disorder"):
  with members:
    - "geocoding without geotags a text based approach for reddit"
    - "not just depressed bipolar disorder prediction on reddit"
    - "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-6 (induced by source paper "multi task learning for argumentation mining in low resource settings"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"
    - "using argument based features to predict and analyse review helpfulness"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "parsing argumentation structures in persuasive essays"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-7 (induced by source paper "carepre an intelligent clinical decision assistance system"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "towards better analysis of deep convolutional neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "mime multilevel medical embedding of electronic health records for predictive healthcare"):
  with members:
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "hadamard product for low rank bilinear pooling"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "compact bilinear pooling"
    - "learning to diagnose with lstm recurrent neural networks"
    - "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)