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: "predictive linguistic features of schizophrenia"
  Abstract: Schizophrenia is one of the most disabling and difficult to treat of all human medical/health conditions, ranking in the top ten causes of disability worldwide. It has been a puzzle in part due to difficulty in identifying its basic, fundamental components. Several studies have shown that some manifestations of schizophrenia (e.g., the negative symptoms that include blunting of speech prosody, as well as the disorganization symptoms that lead to disordered language) can be understood from the perspective of linguistics. However, schizophrenia research has not kept pace with technologies in computational linguistics, especially in semantics and pragmatics. As such, we examine the writings of schizophrenia patients analyzing their syntax, semantics and pragmatics. In addition, we analyze tweets of (self pro-claimed) schizophrenia patients who publicly discuss their diagnoses. For writing samples dataset, syntactic features are found to be the most successful in classification whereas for the less structured Twitter dataset, a combination of features performed the best.

Connected through hyperedge HE-1 (induced by source paper "a review of automated speech and language features for assessment of cognitive and thought disorders"):
  with members:
    - "objective assessment of social skills using automated language analysis for identification of schizophrenia and bipolar disorder"
    - "investigating the effects of word substitution errors on sentence embeddings"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "a review of language and speech features for cognitive linguistic assessment"):
  with members:
    - "objective assessment of social skills using automated language analysis for identification of schizophrenia and bipolar disorder"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "objective assessment of social skills using automated language analysis for identification of schizophrenia and bipolar disorder"):
  with members:
    - "not just depressed bipolar disorder prediction on reddit"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "efficient estimation of word representations in vector space"

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)