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: "pattern recognition and memory mapping using mirroring neural networks"
  Abstract: In this paper, we present a new kind of learning implementation to recognize the patterns using the concept of Mirroring Neural Network (MNN) which can extract information from distinct sensory input patterns and perform pattern recognition tasks. It is also capable of being used as an advanced associative memory wherein image data is associated with voice inputs in an unsupervised manner. Since the architecture is hierarchical and modular it has the potential of being used to devise learning engines of ever increasing complexity.

Connected through hyperedge HE-1 (induced by source paper "learning discriminative features using encoder decoder type deep neural nets"):
  with members:
    - "kernel principal component analysis and its applications in face recognition and active shape models"
    - "deep lambertian networks"
    - "deep big simple neural nets for handwritten digit recognition"
    - "a mirroring theorem and its application to a new method of unsupervised hierarchical pattern classification"

Connected through hyperedge HE-2 (induced by source paper "some theorems for feed forward neural networks"):
  with members:
    - "deep learning in neural networks"
    - "a non iterative method of separation of points by planes in n dimensions and its application"
    - "a mirroring theorem and its application to a new method of unsupervised hierarchical pattern classification"

Connected through hyperedge HE-3 (induced by source paper "a mirroring theorem and its application to a new method of unsupervised hierarchical pattern classification"):
  with members:
    - "dimensionality reduction and reconstruction using mirroring neural networks and object recognition based on reduced dimension characteristic vector"
    - "automatic pattern classification by unsupervised learning using dimensionality reduction of data with mirroring 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.AI(Artificial Intelligence)