human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "unsupervised online multitask learning of behavioral sentence embeddings"
  Abstract: Unsupervised learning has been an attractive method for easily deriving meaningful data representations from vast amounts of unlabeled data. These representations, or embeddings, often yield superior results in many tasks, whether used directly or as features in subsequent training stages. However, the quality of the embeddings is highly dependent on the assumed knowledge in the unlabeled data and how the system extracts information without supervision. Domain portability is also very limited in unsupervised learning, often requiring re-training on other in-domain corpora to achieve robustness. In this work we present a multitask paradigm for unsupervised contextual learning of behavioral interactions which addresses unsupervised domain adaption. We introduce an online multitask objective into unsupervised learning and show that sentence embeddings generated through this process increases performance of affective tasks.

Connected through hyperedge HE-1 (induced by source paper "linking emotions to behaviors through deep transfer learning"):
  with members:
    - "the priori emotion dataset linking mood to emotion detected in the wild"
    - "characterizing types of convolution in deep convolutional recurrent neural networks for robust speech emotion recognition"
    - "unsupervised latent behavior manifold learning from acoustic features audio2behavior"
    - "sparsely connected and disjointly trained deep neural networks for low resource behavioral annotation acoustic classification in couples therapy"
    - "emotional intensity analysis in bipolar subjects"
    - "neural speech recognizer acoustic to word lstm model for large vocabulary speech recognition"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "predicting behavior in cancer afflicted patient and spouse interactions using speech and language"):
  with members:
    - "sparsely connected and disjointly trained deep neural networks for low resource behavioral annotation acoustic classification in couples therapy"

Question: Please tell me which class the center node 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.CL(Computation and Language)