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: "triplespin a generic compact paradigm for fast machine learning computations"
  Abstract: We present a generic compact computational framework relying on structured random matrices that can be applied to speed up several machine learning algorithms with almost no loss of accuracy. The applications include new fast LSH-based algorithms, efficient kernel computations via random feature maps, convex optimization algorithms, quantization techniques and many more. Certain models of the presented paradigm are even more compressible since they apply only bit matrices. This makes them suitable for deploying on mobile devices. All our findings come with strong theoretical guarantees. In particular, as a byproduct of the presented techniques and by using relatively new Berry-Esseen-type CLT for random vectors, we give the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the $\textbf{HD}_{3}\textbf{HD}_{2}\textbf{HD}_{1}$ structured matrix ("Practical and Optimal LSH for Angular Distance"). These guarantees as well as theoretical results for other aforementioned applications follow from the same general theoretical principle that we present in the paper. Our structured family contains as special cases all previously considered structured schemes, including the recently introduced $P$-model. Experimental evaluation confirms the accuracy and efficiency of TripleSpin matrices.

Connected through hyperedge HE-1 (induced by source paper "kerdock codes determine unitary 2 designs"):
  with members:
    - "compressed neighbour discovery using sparse kerdock matrices"
    - "low rank matrix recovery from clifford orbits"
    - "reed muller sensing matrices and the lasso"

Connected through hyperedge HE-2 (induced by source paper "orthogonal random features"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "practical and optimal lsh for angular distance"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-3 (induced by source paper "binary embeddings with structured hashed projections"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "compressing neural networks with the hashing trick"
    - "binary embedding fundamental limits and fast algorithm"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "on binary embedding using circulant matrices"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "the loss surfaces of multilayer networks"
    - "fast training of convolutional networks through ffts"

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)