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: "blackbox a procedure for parallel optimization of expensive black box functions"
  Abstract: This note provides a description of a procedure that is designed to efficiently optimize expensive black-box functions. It uses the response surface methodology by incorporating radial basis functions as the response model. A simple method based on a Latin hypercube is used for initial sampling. A modified version of CORS algorithm with space rescaling is used for the subsequent sampling. The procedure is able to scale on multicore processors by performing multiple function evaluations in parallel. The source code of the procedure is written in Python.

Connected through hyperedge HE-1 (induced by source paper "the heidelberg spiking datasets for the systematic evaluation of spiking neural networks"):
  with members:
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "deep networks incorporating spiking neural dynamics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "supervised learning in spiking neural networks for precise temporal encoding"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "suspicion free adversarial attacks on clustering algorithms"):
  with members:
    - "is data clustering in adversarial settings secure"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "intriguing properties of neural networks"

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.MS(Mathematical Software)