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 "linking emotions to behaviors through deep transfer learning"):
  Abstract: Human behavior refers to the way humans act and interact. Understanding human behavior is a cornerstone of observational practice, especially in psychotherapy. An important cue of behavior analysis is the dynamical changes of emotions during the conversation. Domain experts integrate emotional information in a highly nonlinear manner, thus, it is challenging to explicitly quantify the relationship between emotions and behaviors. In this work, we employ deep transfer learning to analyze their inferential capacity and contextual importance. We first train a network to quantify emotions from acoustic signals and then use information from the emotion recognition network as features for behavior recognition. We treat this emotion-related information as behavioral primitives and further train higher level layers towards behavior quantification. Through our analysis, we find that emotion-related information is an important cue for behavior recognition. Further, we investigate the importance of emotional-context in the expression of behavior by constraining (or not) the neural networks' contextual view of the data. This demonstrates that the sequence of emotions is critical in behavior expression. To achieve these frameworks we employ hybrid architectures of convolutional networks and recurrent networks to extract emotion-related behavior primitives and facilitate automatic behavior recognition from speech.

Includes members:
  Member "the priori emotion dataset linking mood to emotion detected in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "controlling for confounders in multimodal emotion classification via adversarial learning")
  Member "unsupervised online multitask learning of behavioral sentence embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting behavior in cancer afflicted patient and spouse interactions using speech and language")
  Member "characterizing types of convolution in deep convolutional recurrent neural networks for robust speech emotion recognition":
    also connected through other hyperedges:
      (none)
  Member "unsupervised latent behavior manifold learning from acoustic features audio2behavior":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural predictive coding using convolutional neural networks toward unsupervised learning of speaker characteristics")
  Member "sparsely connected and disjointly trained deep neural networks for low resource behavioral annotation acoustic classification in couples therapy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predicting behavior in cancer afflicted patient and spouse interactions using speech and language")
  Member "emotional intensity analysis in bipolar subjects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when to intervene detecting abnormal mood using everyday smartphone conversations")
  Member "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for audio signal processing")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)