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: "facial landmark detection using structured output deep neural networks"
  Abstract: Facial landmark detection is an important step for many perception tasks such as face recognition and facial analysis. Regression-based methods have shown a large success. In particular, deep neural networks (DNN) has demonstrated a strong capability to model the high non-linearity between the face image and the face shape. In this paper, we tackle this task as a structured output problem, where we exploit the strong dependencies that lie between the outputs. Beside learning a regression mapping function from the input to the output, we learn, in an unsupervised way, the inter-dependencies between the outputs. For this, we propose a generic regression framework for structured output problems. Our framework allows a successful incorporation of learning the output structure into DNN using the pre-training trick. We apply our method on a facial landmark detection task, where the output is strongly structured. We evaluate our DNN, named Input/Output Deep Architecture (IODA), on two public challenging datasets: LFPW and HELEN. We show that IODA outperforms traditional deep architectures.

Connected through hyperedge HE-1 (induced by source paper "deep recurrent regression for facial landmark detection"):
  with members:
    - "facial feature tracking under varying facial expressions and face poses based on restricted boltzmann machines"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "learning deconvolution network for semantic segmentation"
    - "facial feature point detection a comprehensive survey"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "show and tell a neural image caption generator"

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)