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: "cost sensitive label embedding for multi label classification"
  Abstract: Label embedding (LE) is an important family of multi-label classification algorithms that digest the label information jointly for better performance. Different real-world applications evaluate performance by different cost functions of interest. Current LE algorithms often aim to optimize one specific cost function, but they can suffer from bad performance with respect to other cost functions. In this paper, we resolve the performance issue by proposing a novel cost-sensitive LE algorithm that takes the cost function of interest into account. The proposed algorithm, cost-sensitive label embedding with multidimensional scaling (CLEMS), approximates the cost information with the distances of the embedded vectors by using the classic multidimensional scaling approach for manifold learning. CLEMS is able to deal with both symmetric and asymmetric cost functions, and effectively makes cost-sensitive decisions by nearest-neighbor decoding within the embedded vectors. We derive theoretical results that justify how CLEMS achieves the desired cost-sensitivity. Furthermore, extensive experimental results demonstrate that CLEMS is significantly better than a wide spectrum of existing LE algorithms and state-of-the-art cost-sensitive algorithms across different cost functions.

Connected through hyperedge HE-1 (induced by source paper "multi label classification with optimal thresholding for multi composition spectroscopic analysis"):
  with members:
    - "correlated logistic model with elastic net regularization for multilabel image classification"
    - "how is a data driven approach better than random choice in label space division for multi label classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "deep convolutional ranking for multilabel image annotation"

Connected through hyperedge HE-2 (induced by source paper "lnemlc label network embeddings for multi label classifiation"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "a survey on network embedding"
    - "a scikit based python environment for performing multi label classification"
    - "hcp a flexible cnn framework for multi label image classification"
    - "node2vec scalable feature learning for networks"
    - "how is a data driven approach better than random choice in label space division for multi label classification"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-3 (induced by source paper "a scikit based python environment for performing multi label classification"):
  with members:
    - "mlc toolbox a matlab octave library for multi label classification"
    - "how is a data driven approach better than random choice in label space division for multi label classification"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "deep semantic ranking based hashing for multi label image retrieval"

Connected through hyperedge HE-4 (induced by source paper "multi label learning with global and local label correlation"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "large scale multi label learning with missing labels"
    - "exact matrix completion via convex optimization"

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)