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 learners for multiclass problems"
  Abstract: The fundamental theorem of statistical learning states that for binary classification problems, any Empirical Risk Minimization (ERM) learning rule has close to optimal sample complexity. In this paper we seek for a generic optimal learner for multiclass prediction. We start by proving a surprising result: a generic optimal multiclass learner must be improper, namely, it must have the ability to output hypotheses which do not belong to the hypothesis class, even though it knows that all the labels are generated by some hypothesis from the class. In particular, no ERM learner is optimal. This brings back the fundmamental question of "how to learn"? We give a complete answer to this question by giving a new analysis of the one-inclusion multiclass learner of Rubinstein et al (2006) showing that its sample complexity is essentially optimal. Then, we turn to study the popular hypothesis class of generalized linear classifiers. We derive optimal learners that, unlike the one-inclusion algorithm, are computationally efficient. Furthermore, we show that the sample complexity of these learners is better than the sample complexity of the ERM rule, thus settling in negative an open question due to Collins (2005).

Connected through hyperedge HE-1 (induced by source paper "the optimal approximation factor in density estimation"):
  with members:
    - "fast and sample near optimal algorithms for learning multidimensional histograms"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "sample efficient learning of mixtures"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "algorithmic stability for adaptive data analysis"
    - "near optimal density estimation in near linear time using variable width histograms"
    - "density estimation in linear time"

Connected through hyperedge HE-2 (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"
    - "multiclass learnability and the erm principle"
    - "regularization techniques for learning with matrices"

Connected through hyperedge HE-3 (induced by source paper "learning simple auctions"):
  with members:
    - "do prices coordinate markets"
    - "the sample complexity of revenue maximization"
    - "the optimal sample complexity of pac learning"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "learning economic parameters from revealed preferences"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-4 (induced by source paper "do prices coordinate markets"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the optimal sample complexity of pac learning"
    - "ironing in the dark"
    - "learning economic parameters from revealed preferences"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "optimal provision after wait in healthcare"
    - "ascending auctions and walrasian equilibrium"

Connected through hyperedge HE-5 (induced by source paper "on statistical learning via the lens of compression"):
  with members:
    - "adaptive learning with robust generalization guarantees"
    - "nearly optimal classification for semimetrics"
    - "sample compression schemes for vc classes"
    - "multiclass learnability and the erm principle"
    - "a geometric approach to sample compression"

Connected through hyperedge HE-6 (induced by source paper "learning from rational behavior predicting solutions to unknown linear programs"):
  with members:
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "achieving target equilibria in network routing games without knowing the latency functions"
    - "learning what s going on reconstructing preferences and priorities from opaque transactions"
    - "efficiently learning from revealed preference"

Connected through hyperedge HE-7 (induced by source paper "labeled compression schemes for extremal classes"):
  with members:
    - "teaching and compressing for low vc dimension"
    - "sample compression schemes for vc classes"
    - "bounding embeddings of vc classes into maximum classes"
    - "shattering graph orientations and connectivity"
    - "a geometric approach to sample compression"

Connected through hyperedge HE-8 (induced by source paper "sample compression schemes for vc classes"):
  with members:
    - "the optimal sample complexity of pac learning"
    - "teaching and compressing for low vc dimension"
    - "a geometric approach to sample compression"
    - "simple strategies for large zero sum games with applications to complexity theory"

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)