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: "topic modeling based multi modal depression detection"
  Abstract: Major depressive disorder is a common mental disorder that affects almost 7% of the adult U.S. population. The 2017 Audio/Visual Emotion Challenge (AVEC) asks participants to build a model to predict depression levels based on the audio, video, and text of an interview ranging between 7-33 minutes. Since averaging features over the entire interview will lose most temporal information, how to discover, capture, and preserve useful temporal details for such a long interview are significant challenges. Therefore, we propose a novel topic modeling based approach to perform context-aware analysis of the recording. Our experiments show that the proposed approach outperforms context-unaware methods and the challenge baselines for all metrics.

Connected through hyperedge HE-1 (induced by source paper "vision based body gesture meta features for affective computing"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "motion feature augmented recurrent neural network for skeleton based dynamic hand gesture recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-2 (induced by source paper "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"):
  with members:
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "on many to many mapping between concordance correlation coefficient and mean square error"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "densely connected convolutional networks"
    - "openxbow introducing the passau open source crossmodal bag of words toolkit"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "measuring depression symptom severity from spoken language and 3d facial expressions"):
  with members:
    - "deep contextualized word representations"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "semantics derived automatically from language corpora contain human like biases"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "character aware neural language models"
    - "distributed representations of sentences and documents"

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)