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: "evolutionary cost sensitive extreme learning machine and subspace extension"
  Abstract: Conventional extreme learning machines solve a Moore-Penrose generalized inverse of hidden layer activated matrix and analytically determine the output weights to achieve generalized performance, by assuming the same loss from different types of misclassification. The assumption may not hold in cost-sensitive recognition tasks, such as face recognition based access control system, where misclassifying a stranger as a family member and allowed to enter the house may result in more serious disaster than misclassifying a family member as a stranger and not allowed to enter. Though recent cost-sensitive learning can reduce the total loss with a given cost matrix that quantifies how severe one type of mistake against another type of mistake, in many realistic cases the cost matrix is unknown to users. Motivated by these concerns, this paper proposes an evolutionary cost-sensitive extreme learning machine (ECSELM), with the following merits: 1) to our best knowledge, it is the first proposal of cost-sensitive ELM; 2) it well addresses the open issue of how to define the cost matrix in cost-sensitive learning tasks; 3) an evolutionary backtracking search algorithm is induced for adaptive cost matrix optimization. Extensively, an ECSLDA method is generalized by coupling with cost-sensitive subspace learning. Experiments in a variety of cost-sensitive tasks well demonstrate the efficiency and effectiveness of the proposed approaches, specifically, 5%~10% improvements in classification are obtained on several datasets compared with ELMs; the computational efficiency is also 10 times faster than cost-sensitive subspace learning methods.

Connected through hyperedge HE-1 (induced by source paper "svm and elm who wins object recognition with deep convolutional features from imagenet"):
  with members:
    - "domain adaptation extreme learning machines for drift compensation in e nose systems"
    - "naive deep face recognition touching the limit of lfw benchmark or not"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "multi column deep neural networks for image classification"

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)