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: "fast computation of the performance evaluation of biometric systems application to multibiometrics"
  Abstract: The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.

Connected through hyperedge HE-1 (induced by source paper "a survey on modality characteristics performance evaluation metrics and security for traditional and wearable biometric systems"):
  with members:
    - "survey on synchrophasor data quality and cybersecurity challenges and evaluation of their interdependencies"
    - "secure management of low power fitness trackers"
    - "secure management of low power fitness trackers"
    - "handwritten signature verification using hand worn devices"
    - "evolution toward 5g multi tier cellular wireless networks an interference management perspective"
    - "an overview of face liveness detection"
    - "enhancing the accuracy of biometric feature extraction fusion using gabor filter and mahalanobis distance algorithm"
    - "bad data injection attack and defense in electricity market using game theory study"

Connected through hyperedge HE-2 (induced by source paper "neuron pruning for compressing deep networks using maxout architectures"):
  with members:
    - "optimistic and pessimistic neural networks for scene and object recognition"
    - "learning the number of neurons in deep networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "predicting parameters in deep learning"

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.DS(Data Structures and Algorithms)