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: "nonlinear dimensionality reduction of data by deep distributed random samplings"
  Abstract: It is believed that if machine can learn human-level invariant semantic concepts from highly-variant real-world data, the real artificial intelligence is able to be touched. If we encode the data and concepts compactly by for example 0 and 1, the golden pathway is a problem of reducing high-dimensional codes to low-dimensional representations. Dimensionality reduction, as a fundamental problem of machine learning---one of the central fields of artificial intelligence, has been intensively studied, where classification and clustering are two special cases of dimensionality reduction that reduce high-dimensional data to discrete points. Multilayer neural networks have demonstrated their great power on dimensionality reduction, which triggered the recent breakthrough of artificial intelligence. However, current multilayer neural networks are limited to large-scale problems, and their training methods are so far complicated and time-consuming. Here we describe a simple multilayer network for dimensionality reduction that each layer of the network is a group of mutually independent k-centers clusterings. We find that the network can be trained successfully layer-by-layer by simply assigning the centers of each clustering by randomly sampled data points from the input. Our results show that the described simple method outperformed 7 well-known dimensionality reduction methods on both very small-scale biomedical data and large-scale image and document data, with much less training time than multilayer neural networks on large-scale data. Our findings imply that if properly designed, a very simple training method can make multilayer networks work well on a wide range of data. Furthermore, given the broad use of simple methods, the described method, which may be easily understood without the domain knowledge, will have a bright future in many branches of science.

Connected through hyperedge HE-1 (induced by source paper "universal background sparse coding and multilayer bootstrap network for speaker recognition"):
  with members:
    - "multilayer bootstrap network for unsupervised speaker recognition"
    - "multilayer bootstrap 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.LG(Machine Learning)