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 learnability and the erm principle"
  Abstract: We study the sample complexity of multiclass prediction in several learning settings. For the PAC setting our analysis reveals a surprising phenomenon: In sharp contrast to binary classification, we show that there exist multiclass hypothesis classes for which some Empirical Risk Minimizers (ERM learners) have lower sample complexity than others. Furthermore, there are classes that are learnable by some ERM learners, while other ERM learners will fail to learn them. We propose a principle for designing good ERM learners, and use this principle to prove tight bounds on the sample complexity of learning {\em symmetric} multiclass hypothesis classes---classes that are invariant under permutations of label names. We further provide a characterization of mistake and regret bounds for multiclass learning in the online setting and the bandit setting, using new generalizations of Littlestone's dimension.

Connected through hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles"):
  with members:
    - "efficient and robust algorithms for adversarial linear contextual bandits"
    - "introduction to online convex optimization"
    - "comments on the du kakade wang yang lower bounds"
    - "is a good representation sufficient for sample efficient reinforcement learning"
    - "model selection for contextual bandits"
    - "contextual bandits with surrogate losses margin bounds and efficient algorithms"
    - "new insights into bootstrapping for bandits"
    - "practical contextual bandits with regression oracles"

Connected through hyperedge HE-2 (induced by source paper "model selection for contextual bandits"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "estimating learnability in the sublinear data regime"
    - "learning to optimize under non stationarity"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "practical contextual bandits with regression oracles"
    - "make the minority great again first order regret bound for contextual bandits"
    - "provably optimal algorithms for generalized linear contextual bandits"

Connected through hyperedge HE-3 (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"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a learning framework for distribution based game theoretic solution concepts"):
  with members:
    - "forming probably stable communities with limited interactions"
    - "envy free classification"
    - "statistical cost sharing"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "hedonic games with graph restricted communication"
    - "learning simple auctions"
    - "sample complexity of automated mechanism design"
    - "learning cooperative games"

Connected through hyperedge HE-5 (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"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "adam a method for stochastic optimization"
    - "generalized singular value thresholding"

Connected through hyperedge HE-6 (induced by source paper "minimax bounds for structured prediction"):
  with members:
    - "learning maximum a posteriori perturbation models for structured prediction in polynomial time"
    - "learning latent variable structured prediction models with gaussian perturbations"
    - "on the information theoretic limits of learning ising models"
    - "grammar as a foreign language"
    - "show and tell a neural image caption generator"
    - "search based structured prediction"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-7 (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"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "optimal learners for multiclass problems"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-8 (induced by source paper "vc classes are adversarially robustly learnable but only improperly"):
  with members:
    - "improved generalization bounds for robust learning"
    - "evasion attacks against machine learning at test time"
    - "intriguing properties of neural networks"

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)