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 "emotion recognition in low resource settings an evaluation of automatic feature selection methods"):
  Abstract: Research in automatic emotion recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient technology, which employs a variety of low-power, resource constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions aim at maintaining the user's independence as unobtrusively as possible. In this context, efforts are being made to model human social signals such as emotions, which can aid health monitoring. This paper focuses on emotion recognition from speech data. In order to minimize the system's memory and computational needs, a minimum number of features should be extracted for use in machine learning models. A number of feature set reduction methods exist which seek to find minimal sets of relevant features. We evaluate three different state of the art feature selection methods: Infinite Latent Feature Selection (ILFS), ReliefF and Fisher (generalized Fisher score), and compare them to our recently proposed feature selection method named 'Active Feature Selection' (AFS). The evaluation is performed on three emotion recognition data sets (EmoDB, SAVEE and EMOVO) using two standard speech feature sets (i.e. eGeMAPs and emobase). The results show that similar or better accuracy can be achieved using subsets of features substantially smaller than entire feature set. A machine learning model trained on a smaller feature set will reduce the memory and computational resources of an emotion recognition system which can result in lowering the barriers for use of health monitoring technology.

Includes members:
  Member "convolutional neural networks pretrained on large face recognition datasets for emotion classification from video":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond")
  Member "temporal multimodal fusion for video emotion classification in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "factorized multimodal transformer for multimodal sequential learning")
  Member "infinite latent feature selection a probabilistic latent graph based ranking approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a matlab toolbox for feature importance ranking")
  Member "generalized fisher score for feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")

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.LG(Machine Learning)