human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "depa self supervised audio embedding for depression detection"):
  Abstract: Depression detection research has increased over the last few decades as this disease is becoming a socially-centered problem. One major bottleneck for developing automatic depression detection methods lies on the limited data availability. Recently, pretrained text-embeddings have seen success in sparse data scenarios, while pretrained audio embeddings are rarely investigated. This paper proposes DEPA, a self-supervised, Word2Vec like pretrained depression audio embedding method for depression detection. An encoder-decoder network is used to extract DEPA on sparse-data in-domain (DAIC) and large-data out-domain (switchboard, Alzheimer's) datasets. With DEPA as the audio embedding, performance significantly outperforms traditional audio features regarding both classification and regression metrics. Moreover, we show that large-data out-domain pretraining is beneficial to depression detection performance.

Includes members:
  Member "text based depression detection what triggers an alert":
    also connected through other hyperedges:
      (none)
  Member "mfcc based recurrent neural network for automatic clinical depression recognition and assessment from speech":
    also connected through other hyperedges:
      (none)
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "measuring depression symptom severity from spoken language and 3d facial expressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond mobile apps a survey of technologies for mental well being")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "soundnet learning sound representations from unlabeled video":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")

Question: Please tell me which class the source paper 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.HC(Human-Computer Interaction)