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: "multiclass learning approaches a theoretical comparison with implications"
  Abstract: We theoretically analyze and compare the following five popular multiclass classification methods: One vs. All, All Pairs, Tree-based classifiers, Error Correcting Output Codes (ECOC) with randomly generated code matrices, and Multiclass SVM. In the first four methods, the classification is based on a reduction to binary classification. We consider the case where the binary classifier comes from a class of VC dimension $d$, and in particular from the class of halfspaces over $\reals^d$. We analyze both the estimation error and the approximation error of these methods. Our analysis reveals interesting conclusions of practical relevance, regarding the success of the different approaches under various conditions. Our proof technique employs tools from VC theory to analyze the \emph{approximation error} of hypothesis classes. This is in sharp contrast to most, if not all, previous uses of VC theory, which only deal with estimation error.

Connected through hyperedge HE-1 (induced by source paper "multi action offline policy learning with bayesian optimization"):
  with members:
    - "efficient policy learning from surrogate loss classification reductions"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "doubly robust policy evaluation and learning"

Connected through hyperedge HE-2 (induced by source paper "envy free classification"):
  with members:
    - "probably approximately metric fair learning"
    - "a voting based system for ethical decision making"
    - "learning non discriminatory predictors"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

Connected through hyperedge HE-3 (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"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "efficient large scale video classification"
    - "conditional probability tree estimation analysis and algorithms"
    - "maximum margin multiclass nearest neighbors"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-4 (induced by source paper "a learning problem that is independent of the set theory zfc axioms"):
  with members:
    - "sample compression schemes for vc classes"
    - "multiclass learnability and the erm principle"

Connected through hyperedge HE-5 (induced by source paper "optimal learners for multiclass problems"):
  with members:
    - "multiclass learnability and the erm principle"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-6 (induced by source paper "a scalable stage wise approach to large margin multi class loss based boosting"):
  with members:
    - "on the dual formulation of boosting algorithms"
    - "boosting through optimization of margin distributions"

Connected through hyperedge HE-7 (induced by source paper "a direct approach to multi class boosting and extensions"):
  with members:
    - "on the dual formulation of boosting algorithms"
    - "solving multiclass learning problems via error correcting output codes"

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)