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: "smhd a large scale resource for exploring online language usage for multiple mental health conditions"
  Abstract: Mental health is a significant and growing public health concern. As language usage can be leveraged to obtain crucial insights into mental health conditions, there is a need for large-scale, labeled, mental health-related datasets of users who have been diagnosed with one or more of such conditions. In this paper, we investigate the creation of high-precision patterns to identify self-reported diagnoses of nine different mental health conditions, and obtain high-quality labeled data without the need for manual labelling. We introduce the SMHD (Self-reported Mental Health Diagnoses) dataset and make it available. SMHD is a novel large dataset of social media posts from users with one or multiple mental health conditions along with matched control users. We examine distinctions in users' language, as measured by linguistic and psychological variables. We further explore text classification methods to identify individuals with mental conditions through their language.

Connected through hyperedge HE-1 (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"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a short review of ethical challenges in clinical natural language processing"
    - "multi task learning for mental health using social media text"
    - "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-2 (induced by source paper "dreaddit a reddit dataset for stress analysis in social media"):
  with members:
    - "emo2vec learning generalized emotion representation by multi task training"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "understanding and measuring psychological stress using social media"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "transfer learning for risk classification of social media posts model evaluation study"):
  with members:
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "quantifying mental health from social media with neural user embeddings"
    - "empath understanding topic signals in large scale text"
    - "automating biomedical data science through tree based pipeline optimization"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "multi task learning for mental health using social media text"
    - "natural language processing almost from scratch"
    - "from frequency to meaning vector space models of semantics"

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)