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: "sewa db a rich database for audio visual emotion and sentiment research in the wild"
  Abstract: Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are becoming indispensable part of our life more and more. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation.

Connected through hyperedge HE-1 (induced by source paper "adversarial based neural networks for affect estimations in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"
    - "end to end facial and physiological model for affective computing and applications"
    - "deep autoencoder for combined human pose estimation and body model upscaling"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "adversarial training in affective computing and sentiment analysis recent advances and perspectives"
    - "semi supervised learning with gans manifold invariance with improved inference"

Connected through hyperedge HE-2 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "the omg emotion behavior dataset"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "multimodal generative models for scalable weakly supervised learning"
    - "pomegranate fast and flexible probabilistic modeling in python"

Connected through hyperedge HE-3 (induced by source paper "translation sentiment and voices a computational model to translate and analyse voices from real time video calling"):
  with members:
    - "a comparison of online automatic speech recognition systems and the nonverbal responses to unintelligible speech"
    - "tensor fusion network for multimodal sentiment analysis"
    - "opennmt open source toolkit for neural machine translation"
    - "towards speech to text translation without speech recognition"
    - "recurrent neural network for text classification with multi task learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "linguistic input features improve neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-4 (induced by source paper "avec 2019 workshop and challenge state of mind detecting depression with ai and cross cultural affect recognition"):
  with members:
    - "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"
    - "topic modeling based multi modal depression detection"
    - "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-5 (induced by source paper "clinical depression and affect recognition with emoaudionet"):
  with members:
    - "improving performance and inference on audio classification tasks using capsule networks"
    - "end to end multimodal emotion recognition using deep neural networks"
    - "avec 2016 depression mood and emotion recognition workshop and challenge"
    - "how transferable are features in deep neural networks"
    - "audio classification from time frequency texture"

Connected through hyperedge HE-6 (induced by source paper "on many to many mapping between concordance correlation coefficient and mean square error"):
  with members:
    - "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.HC(Human-Computer Interaction)