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: "learning hidden markov models for regression using path aggregation"
  Abstract: We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for regression. The learning process involves inferring the structure and parameters of a conventional HMM, while simultaneously learning a regression model that maps features that characterize paths through the model to continuous responses. Our results, in both synthetic and biological domains, demonstrate the value of jointly learning the two components of our approach.

Connected through hyperedge HE-1 (induced by source paper "facial landmark detection using structured output deep neural networks"):
  with members:
    - "fcns in the wild pixel level adversarial and constraint based adaptation"
    - "structured prediction energy networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "correlational neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "deep structured output learning for unconstrained text recognition"
    - "deep visual semantic alignments for generating image descriptions"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks regularization for structured output prediction"):
  with members:
    - "cohort of lstm and lexicon verification for handwriting recognition with gigantic lexicon"
    - "fcns in the wild pixel level adversarial and constraint based adaptation"
    - "socializing the semantic gap a comparative survey on image tag assignment refinement and retrieval"
    - "structured prediction energy networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "deep structured output learning for unconstrained text recognition"
    - "deep visual semantic alignments for generating image descriptions"

Connected through hyperedge HE-3 (induced by source paper "input output deep architecture for structured output problems"):
  with members:
    - "representation learning a review and new perspectives"

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.LG(Machine Learning)