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 online relative comparison kernel learning"
  Abstract: Learning a kernel matrix from relative comparison human feedback is an important problem with applications in collaborative filtering, object retrieval, and search. For learning a kernel over a large number of objects, existing methods face significant scalability issues inhibiting the application of these methods to settings where a kernel is learned in an online and timely fashion. In this paper we propose a novel framework called Efficient online Relative comparison Kernel LEarning (ERKLE), for efficiently learning the similarity of a large set of objects in an online manner. We learn a kernel from relative comparisons via stochastic gradient descent, one query response at a time, by taking advantage of the sparse and low-rank properties of the gradient to efficiently restrict the kernel to lie in the space of positive semidefinite matrices. In addition, we derive a passive-aggressive online update for minimally satisfying new relative comparisons as to not disrupt the influence of previously obtained comparisons. Experimentally, we demonstrate a considerable improvement in speed while obtaining improved or comparable accuracy compared to current methods in the online learning setting.

Connected through hyperedge HE-1 (induced by source paper "efficient data analytics on augmented similarity triplets"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "t exponential triplet embedding"
    - "cost effective hits for relative similarity comparisons"
    - "adaptively learning the crowd kernel"
    - "fast set intersection and two patterns matching"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "less but better generalization enhancement of ordinal embedding via distributional margin"):
  with members:
    - "optimal margin distribution machine"
    - "learning concept embeddings with combined human machine expertise"
    - "cost effective hits for relative similarity comparisons"
    - "large margin distribution machine"
    - "adaptively learning the crowd kernel"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-3 (induced by source paper "semi supervised kernel metric learning using relative comparisons"):
  with members:
    - "discriminative clustering with relative constraints"
    - "metric and kernel learning using a linear transformation"

Connected through hyperedge HE-4 (induced by source paper "learning concept embeddings with combined human machine expertise"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "bird species categorization using pose normalized deep convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning fine grained image similarity with deep ranking"
    - "cost effective hits for relative similarity comparisons"
    - "on constrained spectral clustering and its applications"

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)