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: "training deep neural networks with different datasets in the wild the emotion recognition paradigm"
  Abstract: A novel procedure is presented in this paper, for training a deep convolutional and recurrent neural network, taking into account both the available training data set and some information extracted from similar networks trained with other relevant data sets. This information is included in an extended loss function used for the network training, so that the network can have an improved performance when applied to the other data sets, without forgetting the learned knowledge from the original data set. Facial expression and emotion recognition in-the-wild is the test bed application that is used to demonstrate the improved performance achieved using the proposed approach. In this framework, we provide an experimental study on categorical emotion recognition using datasets from a very recent related emotion recognition challenge.

Connected through hyperedge HE-1 (induced by source paper "emotion generation and recognition a stargan approach"):
  with members:
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "photorealistic facial synthesis in the dimensional affect space"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"):
  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"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "the omg emotion behavior dataset"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "audeering s approach to the one minute gradual emotion challenge"
    - "a multi task learning generation framework valence arousal action units primary expressions"

Connected through hyperedge HE-3 (induced by source paper "a personalized affective memory neural model for improving emotion recognition"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "the omg emotion behavior dataset"
    - "lifelong learning of spatiotemporal representations with dual memory recurrent self organization"
    - "unsupervised deep representations for learning audience facial behaviors"
    - "a deep network for arousal valence emotion prediction with acoustic visual cues"
    - "multimodal emotion recognition for one minute gradual emotion challenge"
    - "emotiw 2018 audio video student engagement and group level affect prediction"
    - "geometry guided adversarial facial expression synthesis"

Connected through hyperedge HE-4 (induced by source paper "expression affect action unit recognition aff wild2 multi task learning and arcface"):
  with members:
    - "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"
    - "arcface additive angular margin loss for deep face recognition"
    - "local learning with deep and handcrafted features for facial expression recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge"

Connected through hyperedge HE-5 (induced by source paper "ai in pursuit of happiness finding only sadness multi modal facial emotion recognition challenge"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "generating faces for affect analysis"
    - "an occam s razor view on learning audiovisual emotion recognition with small training sets"
    - "deep facial expression recognition a survey"
    - "photorealistic facial synthesis in the dimensional affect space"

Connected through hyperedge HE-6 (induced by source paper "aff wild database and affwildnet"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "generating faces for affect analysis"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"

Connected through hyperedge HE-7 (induced by source paper "interpretable deep neural networks for facial expression and dimensional emotion recognition in the wild"):
  with members:
    - "expression affect action unit recognition aff wild2 multi task learning and arcface"
    - "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset"
    - "a multi component cnn rnn approach for dimensional emotion recognition in the wild"
    - "transfer learning for action unit recognition"
    - "a multi task learning generation framework valence arousal action units primary expressions"
    - "aff wild2 extending the aff wild database for affect recognition"
    - "joint unsupervised learning of deep representations and image clusters"
    - "going deeper with convolutions"

Connected through hyperedge HE-8 (induced by source paper "a unified deep learning approach for prediction of parkinson s disease"):
  with members:
    - "predicting parkinson s disease using latent information extracted from deep neural networks"
    - "a survey on deep transfer learning"
    - "deep residual learning for image recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)