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: "automated screening for distress a perspective for the future"
  Abstract: Distress is a complex condition which affects a significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide and other forms of psychological morbidity. Compelling evidence supports screening for distress as a means of facilitating early intervention and subsequent improvements in psychological well-being and overall quality of life. Nevertheless, despite the existence of evidence based and easily administered screening tools, for example, the Distress Thermometer, routine screening for distress is yet to achieve widespread implementation. Efforts are intensifying to utilise innovative, cost effective methods now available through emerging technologies in the informatics and computational arenas.

Connected through hyperedge HE-1 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion"):
  with members:
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "direct modelling of speech emotion from raw speech"
    - "attention augmented end to end multi task learning for emotion prediction from speech"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "flipped adversarial autoencoders"
    - "transfer learning for improving speech emotion classification accuracy"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "emotion recognition in speech using cross modal transfer in the wild"

Connected through hyperedge HE-2 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition"):
  with members:
    - "direct modelling of speech emotion from raw speech"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "improving cross corpus speech emotion recognition with adversarial discriminative domain generalization addog"
    - "cross lingual and multilingual speech emotion recognition on english and french"
    - "transferable positive negative speech emotion recognition via class wise adversarial domain adaptation"
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "automating motion correction in multishot mri using generative adversarial networks"
    - "transfer learning for improving speech emotion classification accuracy"

Connected through hyperedge HE-3 (induced by source paper "direct modelling of speech emotion from raw speech"):
  with members:
    - "phonocardiographic sensing using deep learning for abnormal heartbeat detection"
    - "cross corpus speech emotion classification an effective transfer learning technique"
    - "cnn lstm architecture for speech emotion recognition with data augmentation"
    - "variational autoencoders for learning latent representations of speech emotion a preliminary study"
    - "very deep convolutional neural networks for raw waveforms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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.HC(Human-Computer Interaction)