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: "top rank optimization in linear time"
  Abstract: Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the ranking loss by emphasizing more on the error associated with the top ranked instances, leading to a high computational cost that is super-linear in the number of training instances. We propose a highly efficient approach, titled TopPush, for optimizing accuracy at the top that has computational complexity linear in the number of training instances. We present a novel analysis that bounds the generalization error for the top ranked instances for the proposed approach. Empirical study shows that the proposed approach is highly competitive to the state-of-the-art approaches and is 10-100 times faster.

Connected through hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"

Connected through hyperedge HE-2 (induced by source paper "nonlinear classifiers for ranking problems based on kernelized svm"):
  with members:
    - "general framework for binary classification on top samples"
    - "a review on ranking problems in statistical learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-3 (induced by source paper "general framework for binary classification on top samples"):
  with members:
    - "constrained classification and ranking via quantiles"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the limited multi label projection layer"):
  with members:
    - "linknet relational embedding for scene graph"
    - "smooth loss functions for deep top k classification"
    - "predict and constrain modeling cardinality in deep structured prediction"
    - "sparse and constrained attention for neural machine translation"
    - "neural motifs scene graph parsing with global context"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "on support relations and semantic scene graphs"

Connected through hyperedge HE-5 (induced by source paper "an application of multiple instance learning to estimate generalization risk"):
  with members:
    - "multiple instance learning by boosting infinitely many shapelet based classifiers"
    - "multi class svms from tighter data dependent generalization bounds to novel algorithms"

Connected through hyperedge HE-6 (induced by source paper "cold start playlist recommendation with multitask learning"):
  with members:
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "drug selection via joint push and learning to rank"):
  with members:
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-8 (induced by source paper "quality aware network for set to set recognition"):
  with members:
    - "neural aggregation network for video face recognition"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "pooling faces template based face recognition with pooled face images"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "top push video based person re identification"
    - "supervised transformer network for efficient face detection"
    - "face search at scale 80 million gallery"
    - "facenet a unified embedding for face recognition and clustering"

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)