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: "optimizing ranking measures for compact binary code learning"
  Abstract: Hashing has proven a valuable tool for large-scale information retrieval. Despite much success, 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. Here we present a general framework (termed StructHash) that allows one to directly optimize multivariate performance measures. The resulting optimization problem can involve exponentially or infinitely many variables and constraints, which is more challenging than standard structured output learning. To solve the StructHash optimization problem, we use a combination of column generation and cutting-plane techniques. We demonstrate the generality of StructHash by applying it to ranking prediction and image retrieval, and show that it outperforms a few state-of-the-art hashing methods.

Connected through hyperedge HE-1 (induced by source paper "structured learning of binary codes with column generation"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "structboost boosting methods for predicting structured output variables"
    - "positive semidefinite metric learning using boosting like algorithms"
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-2 (induced by source paper "ordinal constrained binary code learning for nearest neighbor search"):
  with members:
    - "learning to hash for indexing big data a survey"
    - "learning hash functions using column generation"
    - "compact hyperplane hashing with bilinear functions"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "deep semantic ranking based hashing for multi label image retrieval"):
  with members:
    - "one weird trick for parallelizing convolutional neural networks"
    - "imagenet large scale visual recognition challenge"
    - "learning fine grained image similarity with deep ranking"
    - "learning hash functions using column generation"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "deep convolutional ranking for multilabel image annotation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "supervised hashing using graph cuts and boosted decision trees"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning hash functions using column generation"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-5 (induced by source paper "midrank learning to rank based on subsequences"):
  with members:
    - "location recognition over large time lags"

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)