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: "generalization error bounds for learning to rank does the length of document lists matter"
  Abstract: We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is \emph{no} degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under $\ell_1$ regularization and faster convergence rates if the loss function is smooth.

Connected through hyperedge HE-1 (induced by source paper "an alternative cross entropy loss for learning to rank"):
  with members:
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-2 (induced by source paper "data dependent generalization bounds for multi class classification"):
  with members:
    - "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"
    - "large scale multi label learning with missing labels"

Connected through hyperedge HE-3 (induced by source paper "a vector contraction inequality for rademacher complexities"):
  with members:
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"
    - "convex learning of multiple tasks and their structure"
    - "regularization techniques for learning with matrices"
    - "a model of inductive bias learning"

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)