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: "adaptive learning in cartesian product of reproducing kernel hilbert spaces"
  Abstract: We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective is to estimate or track nonlinear functions that are supposed to contain multiple components such as i) linear and nonlinear components and ii) high- and low- frequency components. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where two different methods of the author meet: multikernel adaptive filtering and the algorithm of hyperplane projection along affine subspace (HYPASS). In a particular case, the “sum” space of the RKHSs is isomorphic, under a straightforward correspondence, to the product space, and hence the proposed algorithm can also be regarded as an iterative projection method in the sum space. The efficacy of the proposed algorithm is shown by numerical examples.

Connected through hyperedge HE-1 (induced by source paper "barrier certified adaptive reinforcement learning with applications to brushbot navigation"):
  with members:
    - "safe learning of quadrotor dynamics using barrier certificates"
    - "constrained policy optimization"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "practical kernel based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "detection for 5g noma an online adaptive machine learning approach"):
  with members:
    - "a survey on non orthogonal multiple access for 5g networks research challenges and future trends"
    - "how much spectrum is too much in millimeter wave wireless access"
    - "noma in 5g systems exciting possibilities for enhancing spectral efficiency"
    - "non orthogonal multiple access in multi cell networks theory performance and practical challenges"

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)