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: "nonnegative binary matrix factorization with a d wave quantum annealer"
  Abstract: D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output—one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.

Connected through hyperedge HE-1 (induced by source paper "support vector machines on the d wave quantum annealer"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "deep learning using linear support vector machines"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-2 (induced by source paper "on modeling local search with special purpose combinatorial optimization hardware"):
  with members:
    - "detecting multiple communities using quantum annealing on the d wave system"
    - "a tutorial on formulating and using qubo models"
    - "relaxation based coarsening for multilevel hypergraph partitioning"
    - "free energy based reinforcement learning using a quantum processor"
    - "a novel graph based approach for determining molecular similarity"
    - "recent advances in graph partitioning"
    - "advanced coarsening schemes for graph partitioning"
    - "engineering multilevel graph partitioning algorithms"

Connected through hyperedge HE-3 (induced by source paper "quantum annealing based binary compressive sensing with matrix uncertainty"):
  with members:
    - "bilinear adaptive generalized vector approximate message passing"
    - "extending the d wave with support for higher precision coefficients"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "general deviants an analysis of perturbations in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "a quantum computational approach to correspondence problems on point sets"):
  with members:
    - "quantum algorithm implementations for beginners"
    - "fast clique minor generation in chimera qubit connectivity graphs"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (induced by source paper "on post processing the results of quantum optimizers"):
  with members:
    - "extending the d wave with support for higher precision coefficients"
    - "effective optimization using sample persistence a case study on quantum annealers and various monte carlo optimization methods"
    - "stochastic characteristics of qubits and qubit chains on the d wave 2x"

Connected through hyperedge HE-6 (induced by source paper "a tutorial on formulating and using qubo models"):
  with members:
    - "detecting multiple communities using quantum annealing on the d wave system"
    - "computing wasserstein distance for persistence diagrams on a quantum computer"
    - "logical and inequality implications for reducing the size and complexity of quadratic unconstrained binary optimization problems"
    - "integrating tabu search and vlsn search to develop enhanced algorithms a case study using bipartite boolean quadratic programs"

Connected through hyperedge HE-7 (induced by source paper "solving non identifiable latent feature models"):
  with members:
    - "the uk dale dataset domestic appliance level electricity demand and whole house demand from five uk homes"

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)