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: "optimal dictionary for least squares representation"
  Abstract: Dictionary Learning problems are concerned with finding a collection of vectors usually referred to as the dictionary, such that the representation of random vectors with a given distribution using this dictionary is optimal. Most of the recent research in dictionary learning is focused on developing dictionaries which offer sparse representation, i.e., optimal representation in the $\ell_0$ sense. We consider the problem of finding an optimal dictionary with which representation of samples of a random vector on an average is optimal. Optimality of representation is defined in the sense of attaining minimal average $\ell_2$-norm of the coefficient vector used to represent the random vector. With the help of recent results related to rank-one decompositions of real symmetric positive semi-definite matrices, an explicit solution for an $\ell_2$-optimal dictionary is obtained.

Connected through hyperedge HE-1 (induced by source paper "measure of quality of finite dimensional linear systems a frame theoretic view"):
  with members:
    - "gramian based reachability metrics for bilinear networks"
    - "controllability metrics limitations and algorithms for complex networks"

Connected through hyperedge HE-2 (induced by source paper "memristors for the curious outsiders"):
  with members:
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "hierarchical temporal memory using memristor networks a survey"
    - "thermodynamic ram technology stack"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "temporal correlation detection using computational phase change memory"

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)