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: "efficient optimization for sparse gaussian process regression"
  Abstract: We propose an efficient optimization algorithm for selecting a subset of training data to induce sparsity for Gaussian process regression. The algorithm estimates an inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy. The space and time complexity are linear in training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains. Empirical evaluation shows state-of-art performance in discrete cases and competitive results in the continuous case.

Connected through hyperedge HE-1 (induced by source paper "few shot self reminder to overcome catastrophic forgetting"):
  with members:
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "hindsight experience replay"
    - "lifelong learning with dynamically expandable networks"
    - "continual learning with deep generative replay"
    - "encoder based lifelong learning"
    - "continual learning through synaptic intelligence"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "gradient episodic memory for continual learning"

Connected through hyperedge HE-2 (induced by source paper "local gaussian processes for efficient fine grained traffic speed prediction"):
  with members:
    - "shortest path and distance queries on road networks towards bridging theory and practice"

Connected through hyperedge HE-3 (induced by source paper "learning the kernel matrix via predictive low rank approximations"):
  with members:
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "a la carte learning fast kernels"
    - "fastfood approximate kernel expansions in loglinear time"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "algorithms for learning kernels based on centered alignment"
    - "sharp analysis of low rank kernel matrix approximations"

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)