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: "depression and self harm risk assessment in online forums"
  Abstract: Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.

Connected through hyperedge HE-1 (induced by source paper "finding your voice the linguistic development of mental health counselors"):
  with members:
    - "conversations gone awry detecting early signs of conversational failure"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "the social dynamics of language change in online networks"
    - "conversational markers of constructive discussions"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "computational sociolinguistics a survey"

Connected through hyperedge HE-2 (induced by source paper "conversation model fine tuning for classifying client utterances in counseling dialogues"):
  with members:
    - "a hierarchical latent structure for variational conversation modeling"
    - "universal language model fine tuning for text classification"
    - "regularizing and optimizing lstm language models"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "unsupervised pretraining for sequence to sequence learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "building end to end dialogue systems using generative hierarchical neural network models"

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"
    - "multi task learning for mental health using social media text"
    - "detecting and explaining crisis"
    - "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 "fusing visual textual and connectivity clues for studying mental health"):
  with members:
    - "multimodal classification for analysing social media"
    - "mining online social data for detecting social network mental disorders"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"
    - "instagram photos reveal predictive markers of depression"
    - "xgboost a scalable tree boosting system"
    - "identifying depression on twitter"
    - "social media image analysis for public health"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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"
    - "multi task learning for mental health using social media text"
    - "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-6 (induced by source paper "semantic characteristics of schizophrenic speech"):
  with members:
    - "learning word vectors for 157 languages"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-7 (induced by source paper "style obfuscation by invariance"):
  with members:
    - "when coding style survives compilation de anonymizing programmers from executable binaries"
    - "cross lingual distillation for text classification"
    - "the case for being average a mediocrity approach to style masking and author obfuscation"
    - "a 4 nt author attribute anonymity by adversarial training of neural machine translation"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "controlling linguistic style aspects in neural language generation"
    - "controllable invariance through adversarial feature learning"
    - "adversarial multi task learning for text classification"

Connected through hyperedge HE-8 (induced by source paper "smhd a large scale resource for exploring online language usage for multiple mental health conditions"):
  with members:
    - "rsdd time temporal annotation of self reported mental health diagnoses"
    - "a short review of ethical challenges in clinical natural language processing"
    - "detecting and explaining crisis"
    - "triaging content severity in online mental health forums"
    - "inferring individual attributes from search engine queries and auxiliary information"
    - "feature studies to inform the classification of depressive symptoms from twitter data for population health"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "xgboost a scalable tree boosting system"

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)