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 "on the robustness of speech emotion recognition for human robot interaction with deep neural networks"):
  Abstract: Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural network-based architectures were proposed recently and pushed the performance to a new level. However, the applicability of such neural SER models trained only on in-domain data to noisy conditions is currently under-researched. In this work, we evaluate the robustness of state-of-the-art neural acoustic emotion recognition models in human-robot interaction scenarios. We hypothesize that a robot's ego noise, room conditions, and various acoustic events that can occur in a home environment can significantly affect the performance of a model. We conduct several experiments on the iCub robot platform and propose several novel ways to reduce the gap between the model's performance during training and testing in real-world conditions. Furthermore, we observe large improvements in the model performance on the robot and demonstrate the necessity of introducing several data augmentation techniques like overlaying background noise and loudness variations to improve the robustness of the neural approaches.

Includes members:
  Member "reusing neural speech representations for auditory emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis")
  Member "progressive neural networks for transfer learning in emotion recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for human affect recognition insights and new developments")
  Member "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "improved regularization techniques for end to end speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "incorporating end to end speech recognition models for sentiment analysis")
  Member "attentive convolutional neural network based speech emotion recognition a study on the impact of input features signal length and acted speech":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "similarity of speech emotion in different languages revealed by a neural network with attention")
  Member "deep visual foresight for planning robot motion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "training deep networks for facial expression recognition with crowd sourced label distribution":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "expression analysis based on face regions in read world conditions")
  Member "recurrent batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking batch normalization in transformers")

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.RO(Robotics)