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 stage classifier design"
  Abstract: In many classification systems, sensing modalities have different acquisition costs. It is often {\it unnecessary} to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, measurements in a new modality can be acquired at each stage for an additional cost. We seek decision rules to reduce the average measurement acquisition cost. We formulate an empirical risk minimization problem (ERM) for a multi-stage reject classifier, wherein the stage $k$ classifier either classifies a sample using only the measurements acquired so far or rejects it to the next stage where more attributes can be acquired for a cost. To solve the ERM problem, we show that the optimal reject classifier at each stage is a combination of two binary classifiers, one biased towards positive examples and the other biased towards negative examples. We use this parameterization to construct stage-by-stage global surrogate risk, develop an iterative algorithm in the boosting framework and present convergence and generalization results. We test our work on synthetic, medical and explosives detection datasets. Our results demonstrate that substantial cost reduction without a significant sacrifice in accuracy is achievable.

Connected through hyperedge HE-1 (induced by source paper "adversarial encoder multi task decoder for multi stage processes"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "semi supervised learning with deep generative models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-2 (induced by source paper "cost sensitive feature value acquisition using feature relevance"):
  with members:
    - "dynamic feature acquisition using denoising autoencoders"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "the greedy miser learning under test time budgets"

Connected through hyperedge HE-3 (induced by source paper "gradient regularized budgeted boosting"):
  with members:
    - "the greedy miser learning under test time budgets"

Connected through hyperedge HE-4 (induced by source paper "guided processing outperforms duty cycling for energy efficient systems"):
  with members:
    - "feature sharing in cascade detection systems with multiple applications"

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.CV(Computer Vision and Pattern Recognition)