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: "a novel transfer learning method based on common space mapping and weighted domain matching"
  Abstract: In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the common space, the target domain data points are weighted and matched to the target domain in term of distributions. The weighting terms of source domain data points and the target domain classification responses are also regularized by the local reconstruction coefficients. The novel transfer learning framework is evaluated over some benchmark cross-domain data sets, and it outperforms the existing state-of-the-art transfer learning methods.

Connected through hyperedge HE-1 (induced by source paper "a novel image tag completion method based on convolutional neural network"):
  with members:
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "learning convolutional neural network to maximize pos top performance measure"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"

Connected through hyperedge HE-2 (induced by source paper "edos edge assisted offloading system for mobile devices"):
  with members:
    - "dynamic computation offloading for mobile edge computing with energy harvesting devices"
    - "efficient multi user computation offloading for mobile edge cloud computing"

Connected through hyperedge HE-3 (induced by source paper "cross model convolutional neural network for multiple modality data representation"):
  with members:
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "learning convolutional neural network to maximize pos top performance measure"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "supervised cross modal factor analysis for multiple modal data classification"
    - "multiple kernel multivariate performance learning using cutting plane algorithm"
    - "caffe convolutional architecture for fast feature embedding"
    - "a convolutional neural network for modelling sentences"
    - "multimodal similarity preserving hashing"

Connected through hyperedge HE-4 (induced by source paper "learning convolutional neural network to maximize pos top performance measure"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "stochastic learning of multi instance dictionary for earth mover s distance based histogram comparison"
    - "push and pull based epidemic spreading in networks thresholds and deeper insights"
    - "optimizing top precision performance measure of content based image retrieval by learning similarity function"
    - "top rank optimization in linear time"
    - "an evasion and counter evasion study in malicious websites detection"

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)