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: "multi class svms from tighter data dependent generalization bounds to novel algorithms"
  Abstract: This paper studies the generalization performance of multi-class classification algorithms, for which we obtain, for the first time, a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis. The theoretical analysis motivates us to introduce a new multi-class classification machine based on $\ell_p$-norm regularization, where the parameter $p$ controls the complexity of the corresponding bounds. We derive an efficient optimization algorithm based on Fenchel duality theory. Benchmarks on several real-world datasets show that the proposed algorithm can achieve significant accuracy gains over the state of the art.

Connected through hyperedge HE-1 (induced by source paper "learning vector valued functions with local rademacher complexity"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "an overview of gradient descent optimization algorithms"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-2 (induced by source paper "learning vector valued functions with local rademacher complexity and unlabeled data"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "data dependent generalization bounds for multi class classification"
    - "a survey on multi task learning"
    - "orthogonal random features"
    - "a vector contraction inequality for rademacher complexities"
    - "adam a method for stochastic optimization"
    - "generalized singular value thresholding"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-3 (induced by source paper "generalization bounds in the predict then optimize framework"):
  with members:
    - "task based end to end model learning in stochastic optimization"
    - "a vector contraction inequality for rademacher complexities"
    - "optimal learners for multiclass problems"
    - "multiclass learnability and the erm principle"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-4 (induced by source paper "an application of multiple instance learning to estimate generalization risk"):
  with members:
    - "multiple instance learning by boosting infinitely many shapelet based classifiers"
    - "top rank optimization in linear time"

Connected through hyperedge HE-5 (induced by source paper "handling concept drift via model reuse"):
  with members:
    - "hybrid forest a concept drift aware data stream mining algorithm"
    - "introduction to online convex optimization"
    - "robust artificial intelligence and robust human organizations"
    - "adaptive online learning in dynamic environments"
    - "learn on source refine on target a model transfer learning framework with random forests"
    - "concept drift adaptation by exploiting historical knowledge"
    - "fast rates by transferring from auxiliary hypotheses"
    - "a vector contraction inequality for rademacher complexities"

Connected through hyperedge HE-6 (induced by source paper "data dependent generalization bounds for multi class classification"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "a vector contraction inequality for rademacher complexities"
    - "lshtc a benchmark for large scale text classification"
    - "efficient large scale video classification"
    - "conditional probability tree estimation analysis and algorithms"
    - "maximum margin multiclass nearest neighbors"
    - "multiclass learnability and the erm principle"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-7 (induced by source paper "a vector contraction inequality for rademacher complexities"):
  with members:
    - "generalization error bounds for learning to rank does the length of document lists matter"
    - "convex learning of multiple tasks and their structure"
    - "regularization techniques for learning with matrices"
    - "a model of inductive bias learning"

Connected through hyperedge HE-8 (induced by source paper "local rademacher complexity bounds based on covering numbers"):
  with members:
    - "unregularized online learning algorithms with general loss functions"
    - "generalization bounds for metric and similarity learning"
    - "optimistic rates for learning with a smooth loss"

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)