human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "structured learning of binary codes with column generation"):
  Abstract: Hashing methods aim to learn a set of hash functions which map the original features to compact binary codes with similarity preserving in the Hamming space. Hashing has proven a valuable tool for large-scale information retrieval. We propose a column generation based binary code learning framework for data-dependent hash function learning. Given a set of triplets that encode the pairwise similarity comparison information, our column generation based method learns hash functions that preserve the relative comparison relations within the large-margin learning framework. Our method iteratively learns the best hash functions during the column generation procedure. Existing hashing methods optimize over simple objectives such as the reconstruction error or graph Laplacian related loss functions, instead of the performance evaluation criteria of interest---multivariate performance measures such as the AUC and NDCG. Our column generation based method can be further generalized from the triplet loss to a general structured learning based framework that allows one to directly optimize multivariate performance measures. For optimizing general ranking measures, the resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. We use a combination of column generation and cutting-plane techniques to solve the optimization problem. To speed-up the training we further explore stage-wise training and propose to use a simplified NDCG loss for efficient inference. We demonstrate the generality of our method by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.

Includes members:
  Member "fast supervised hashing with decision trees for high dimensional data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probability weighted compact feature for domain adaptive retrieval")
  Member "optimizing ranking measures for compact binary code learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ordinal constrained binary code learning for nearest neighbor search")
  Member "learning hash functions using column generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep multi index hashing for person re identification")
  Member "inductive hashing on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proximity preserving binary code using signed graph cut")
  Member "a general two step approach to learning based hashing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep ordinal hashing with spatial attention")
  Member "structboost boosting methods for predicting structured output variables":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "large margin learning of compact binary image encodings")
  Member "positive semidefinite metric learning using boosting like algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly")
  Member "on the dual formulation of boosting algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "margin maximization as lossless maximal compression")

Question: Please tell me which class the source paper 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)