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 "ai in pursuit of happiness finding only sadness multi modal facial emotion recognition challenge"):
  Abstract: The importance of automated Facial Emotion Recognition (FER) grows the more common human-machine interactions become, which will only continue to increase dramatically with time. A common method to describe human sentiment or feeling is the categorical model the `7 basic emotions', consisting of `Angry', `Disgust', `Fear', `Happiness', `Sadness', `Surprise' and `Neutral'. The `Emotion Recognition in the Wild' (EmotiW) competition is now in its 7th year and has become the standard benchmark for measuring FER performance. The focus of this paper is the EmotiW sub-challenge of classifying videos in the `Acted Facial Expression in the Wild' (AFEW) dataset, consisting of both visual and audio modalities, into one of the above classes. Machine learning has exploded as a research topic in recent years, with advancements in `Deep Learning' a key part of this. Although Deep Learning techniques have been widely applied to the FER task by entrants in previous years, this paper has two main contributions: (i) to apply the latest `state-of-the-art' visual and temporal networks and (ii) exploring various methods of fusing features extracted from the visual and audio elements to enrich the information available to the final model making the prediction. There are a number of complex issues that arise when trying to classify emotions for `in-the-wild' video sequences, which the above two approaches attempt to directly address. There are some positive findings when comparing the results of this paper to past submissions, indicating that further research into the proposed methods and fine-tuning of the models deployed, could result in another step forwards in the field of automated FER.

Includes members:
  Member "affectnet a database for facial expression valence and arousal computing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "exploiting multi cnn features in cnn rnn based dimensional emotion recognition on the omg in the wild dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless attention in convolutional networks for facial expression recognition in the wild")
  Member "training deep neural networks with different datasets in the wild the emotion recognition paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "emotion generation and recognition a stargan approach")
  Member "a multi component cnn rnn approach for dimensional emotion recognition in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysing affective behavior in the first abaw 2020 competition")
  Member "generating faces for affect analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "emotion generation and recognition a stargan approach")
  Member "an occam s razor view on learning audiovisual emotion recognition with small training sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards a general model of knowledge for facial analysis by multi source transfer learning")
  Member "deep facial expression recognition a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")

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.CV(Computer Vision and Pattern Recognition)