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: "fast label embeddings for extremely large output spaces"
  Abstract: Many modern multiclass and multilabel problems are characterized by increasingly large output spaces. For these problems, label embeddings have been shown to be a useful primitive that can improve computational and statistical efficiency. In this work we utilize a correspondence between rank constrained estimation and low dimensional label embeddings that uncovers a fast label embedding algorithm which works in both the multiclass and multilabel settings. The result is a randomized algorithm for partial least squares, whose running time is exponentially faster than naive algorithms. We demonstrate our techniques on two large-scale public datasets, from the Large Scale Hierarchical Text Challenge and the Open Directory Project, where we obtain state of the art results.

Connected through hyperedge HE-1 (induced by source paper "extreme regression for dynamic search advertising"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "attentionxml label tree based attention aware deep model for high performance extreme multi label text classification"
    - "lshtc a benchmark for large scale text classification"
    - "a theoretical analysis of ndcg type ranking measures"
    - "large scale multi label learning with missing labels"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "exact and efficient top k inference for multi target prediction by querying separable linear relational models"):
  with members:
    - "lshtc a benchmark for large scale text classification"
    - "identification of functionally related enzymes by learning to rank methods"
    - "a two step learning approach for solving full and almost full cold start problems in dyadic prediction"
    - "a comparative study of collaborative filtering algorithms"
    - "efficient regularized least squares algorithms for conditional ranking on relational data"
    - "optimal aggregation algorithms for middleware"
    - "top down induction of clustering trees"

Connected through hyperedge HE-3 (induced by source paper "scalable multilabel prediction via randomized methods"):
  with members:
    - "comparing published multi label classifier performance measures to the ones obtained by a simple multi label baseline classifier"
    - "logarithmic time online multiclass prediction"
    - "efficient exact gradient update for training deep networks with very large sparse targets"
    - "fastfood approximate kernel expansions in loglinear time"
    - "least squares revisited scalable approaches for multi class prediction"
    - "simple and deterministic matrix sketching"
    - "weak calibration is computationally hard"
    - "multi label prediction via compressed sensing"

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)