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: "multi task neural networks for personalized pain recognition from physiological signals"
  Abstract: Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.

Connected through hyperedge HE-1 (induced by source paper "detection of real world driving induced affective state using physiological signals and multi view multi task machine learning"):
  with members:
    - "multi task multiple kernel machines for personalized pain recognition from functional near infrared spectroscopy brain signals"
    - "personalized driver stress detection with multi task neural networks using physiological signals"
    - "physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning"

Connected through hyperedge HE-2 (induced by source paper "personalizing intervention probabilities by pooling"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "personalized driver stress detection with multi task neural networks using physiological signals"

Connected through hyperedge HE-3 (induced by source paper "multi task multiple kernel machines for personalized pain recognition from functional near infrared spectroscopy brain signals"):
  with members:
    - "physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning"):
  with members:
    - "adam a method for stochastic optimization"
    - "a tutorial on spectral clustering"

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.CY(Computers and Society)