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 "classifying and visualizing emotions with emotional dan"):
  Abstract: Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN) with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed by the network when making a decision and the results indicate that our EmotionalDAN model is able to correctly identify facial landmarks responsible for expressing the emotions.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "affectnet a database for facial expression valence and arousal computing in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade ef gan progressive facial expression editing with local focuses")
  Member "deep adaptive attention for joint facial action unit detection and face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "region based adversarial synthesis of facial action units")
  Member "facial action unit detection using attention and relation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised unconstrained action unit detection via latent feature domain")
  Member "i know how you feel emotion recognition with facial landmarks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction")
  Member "the indian spontaneous expression database for emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "iiitm face a database for facial attribute detection in constrained and simulated unconstrained environments")
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")
  Member "facial expression recognition using enhanced deep 3d convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "emptransfo a multi head transformer architecture for creating empathetic dialog systems")

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)