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: "when is there a representer theorem vector versus matrix regularizers"
  Abstract: We consider a general class of regularization methods which learn a vector of parameters on the basis of linear measurements. It is well known that if the regularizer is a nondecreasing function of the inner product then the learned vector is a linear combination of the input data. This result, known as the {\em representer theorem}, is at the basis of kernel-based methods in machine learning. In this paper, we prove the necessity of the above condition, thereby completing the characterization of kernel methods based on regularization. We further extend our analysis to regularization methods which learn a matrix, a problem which is motivated by the application to multi-task learning. In this context, we study a more general representer theorem, which holds for a larger class of regularizers. We provide a necessary and sufficient condition for these class of matrix regularizers and highlight them with some concrete examples of practical importance. Our analysis uses basic principles from matrix theory, especially the useful notion of matrix nondecreasing function.

Connected through hyperedge HE-1 (induced by source paper "ockelm kernel extreme learning machine based one class classification using privileged information or koc kernel ridge regression or least square svm with zero bias based one class classification using privileged information"):
  with members:
    - "deep learning under privileged information using heteroscedastic dropout"
    - "privileged information for data clustering"

Connected through hyperedge HE-2 (induced by source paper "graph embedded multi layer kernel extreme learning machine for one class classification or graph embedded multi layer kernel ridge regression for one class classification"):
  with members:
    - "on the construction of extreme learning machine for online and offline one class classification an expanded toolbox"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "nonparametric stochastic compositional gradient descent for q learning in continuous markov decision problems"):
  with members:
    - "learning from conditional distributions via dual kernel embeddings"
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep reinforcement learning"
    - "modelling transition dynamics in mdps with rkhs embeddings"

Connected through hyperedge HE-4 (induced by source paper "multi layer kernel ridge regression for one class classification"):
  with members:
    - "deep learning in neural networks"
    - "deep kernel learning"

Connected through hyperedge HE-5 (induced by source paper "when is there a representer theorem nondifferentiable regularisers and banach spaces"):
  with members:
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "stochastic policy gradient ascent in reproducing kernel hilbert spaces"):
  with members:
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "a survey on multi task learning"):
  with members:
    - "learning to multitask"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "learning multiple visual tasks while discovering their structure"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "safe screening for multi task feature learning with multiple data matrices"

Connected through hyperedge HE-8 (induced by source paper "convex discriminative multitask clustering"):
  with members:
    - "a general iterative shrinkage and thresholding algorithm for non convex regularized optimization problems"
    - "a convex formulation for learning task relationships in multi task learning"
    - "revisiting k means new algorithms via bayesian nonparametrics"
    - "clustered multi task learning a convex formulation"

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)