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: "analysing affective behavior in the first abaw 2020 competition"
  Abstract: The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior task. For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with the IEEE Conference on Face and Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics and present the baseline methodologies and the obtained results when these are applied to each Challenge. More information regarding the Competition and details for how to access the utilized database, are provided in the Competition site: this http URL

Connected through hyperedge HE-1 (induced by source paper "two stream aural visual affect analysis in the wild"):
  with members:
    - "multi label class balancing algorithm for action unit detection"
    - "fau facial expressions valence and arousal a multi task solution"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "context aware emotion recognition networks"
    - "retinaface single stage dense face localisation in the wild"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "arcface additive angular margin loss for deep face recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-2 (induced by source paper "m 3 t multi modal continuous valence arousal estimation in the wild"):
  with members:
    - "facial affect recognition in the wild using multi task learning convolutional network"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "large scale visual speech recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-3 (induced by source paper "emotion recognition for in the wild videos"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "retinaface single stage dense face localisation in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "pytorch an imperative style high performance deep learning library"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-4 (induced by source paper "multitask emotion recognition with incomplete labels"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "face behavior a la carte expressions affect and action units in a single network"
    - "mimamo net integrating micro and macro motion for video emotion recognition"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "the omg emotion behavior dataset"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "emotionet challenge recognition of facial expressions of emotion in the wild"
    - "from facial expression recognition to interpersonal relation prediction"

Connected through hyperedge HE-5 (induced by source paper "unique class group based multi label balancing optimizer for action unit detection"):
  with members:
    - "fau facial expressions valence and arousal a multi task solution"
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "deep residual learning for image recognition"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "affective expression analysis in the wild using multi task temporal statistical deep learning model"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep residual learning for image recognition"

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.LG(Machine Learning)