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: "structured adaptive and random spinners for fast machine learning computations"
  Abstract: We consider an efficient computational framework for speeding up several machine learning algorithms with almost no loss of accuracy. The proposed framework relies on projections via structured matrices that we call Structured Spinners, which are formed as products of three structured matrix-blocks that incorporate rotations. The approach is highly generic, i.e. i) structured matrices under consideration can either be fully-randomized or learned, ii) our structured family contains as special cases all previously considered structured schemes, iii) the setting extends to the non-linear case where the projections are followed by non-linear functions, and iv) the method finds numerous applications including kernel approximations via random feature maps, dimensionality reduction algorithms, new fast cross-polytope LSH techniques, deep learning, convex optimization algorithms via Newton sketches, quantization with random projection trees, and more. The proposed framework comes with theoretical guarantees characterizing the capacity of the structured model in reference to its unstructured counterpart and is based on a general theoretical principle that we describe in the paper. As a consequence of our theoretical analysis, we provide the first theoretical guarantees for one of the most efficient existing LSH algorithms based on the HD3HD2HD1 structured matrix [Andoni et al., 2015]. The exhaustive experimental evaluation confirms the accuracy and efficiency of structured spinners for a variety of different applications.

Connected through hyperedge HE-1 (induced by source paper "efficient data analytics on augmented similarity triplets"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "t exponential triplet embedding"
    - "efficient online relative comparison kernel learning"
    - "cost effective hits for relative similarity comparisons"
    - "adaptively learning the crowd kernel"
    - "fast set intersection and two patterns matching"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "bolt accelerated data mining with fast vector compression"):
  with members:
    - "polysemous codes"
    - "orthogonal random features"
    - "loss aware binarization of deep networks"
    - "generalized residual vector quantization for large scale data"
    - "learning both weights and connections for efficient neural networks"
    - "acdc a structured efficient linear layer"
    - "practical and optimal lsh for angular distance"
    - "quantization based fast inner product search"

Connected through hyperedge HE-3 (induced by source paper "streaming binary sketching based on subspace tracking and diagonal uniformization"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "optimizing affinity based binary hashing using auxiliary coordinates"
    - "learning to hash for indexing big data a survey"
    - "practical and optimal lsh for angular distance"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-4 (induced by source paper "local group invariant representations via orbit embeddings"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "learning from conditional distributions via dual kernel embeddings"
    - "group equivariant convolutional networks"
    - "learning with group invariant features a kernel perspective"
    - "on invariance and selectivity in representation learning"
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "fastfood approximate kernel expansions in loglinear time"

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)