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 approximation of rotations and hessians matrices"
  Abstract: A new method to represent and approximate rotation matrices is introduced. The method represents approximations of a rotation matrix $Q$ with linearithmic complexity, i.e. with $\frac{1}{2}n\lg(n)$ rotations over pairs of coordinates, arranged in an FFT-like fashion. The approximation is "learned" using gradient descent. It allows to represent symmetric matrices $H$ as $QDQ^T$ where $D$ is a diagonal matrix. It can be used to approximate covariance matrix of Gaussian models in order to speed up inference, or to estimate and track the inverse Hessian of an objective function by relating changes in parameters to changes in gradient along the trajectory followed by the optimization procedure. Experiments were conducted to approximate synthetic matrices, covariance matrices of real data, and Hessian matrices of objective functions involved in machine learning problems.

Connected through hyperedge HE-1 (induced by source paper "learning fast sparsifying transforms"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "trainlets dictionary learning in high dimensions"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "tunable efficient unitary neural networks eunn and their application to rnns"):
  with members:
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "orthogonal rnns and long memory tasks"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "unitary evolution recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "fast orthonormal sparsifying transforms based on householder reflectors"):
  with members:
    - "on the computational intractability of exact and approximate dictionary learning"
    - "more algorithms for provable dictionary learning"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "learning computationally efficient dictionaries and their implementation as fast transforms"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used dictionaries"
    - "dictionary learning for deblurring and digital zoom"

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)