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 "hierarchical annotation of images with two alternative forced choice metric learning"):
  Abstract: Many tasks such as retrieval and recommendations can significantly benefit from structuring the data, commonly in a hierarchical way. To achieve this through annotations of high dimensional data such as images or natural text can be significantly labor intensive. We propose an approach for uncovering the hierarchical structure of data based on efficient discriminative testing rather than annotations of individual datapoints. Using two-alternative-forced-choice (2AFC) testing and deep metric learning we achieve embedding of the data in semantic space where we are able to successfully hierarchically cluster. We actively select triplets for the 2AFC test such that the modeling process is highly efficient with respect to the number of tests presented to the annotator. We empirically demonstrate the feasibility of the method by confirming the shape bias on synthetic data and extract hierarchical structure on the Fashion-MNIST dataset to a finer granularity than the original labels.

Includes members:
  Member "crowd counting using deep recurrent spatial aware network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "deep metric learning with hierarchical triplet loss":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting training strategies and generalization performance in deep metric learning")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fine grained image classification via combining vision and language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained visual textual representation learning")
  Member "low rank bilinear pooling for fine grained classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fine grained visual textual representation learning")
  Member "disc deep image saliency computing via progressive representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection")
  Member "localizing by describing attribute guided attention localization for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition")

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)