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 "end to end efficient representation learning via cascading combinatorial optimization"):
  Abstract: We develop hierarchically quantized efficient embedding representations for similarity-based search and show that this representation provides not only the state of the art performance on the search accuracy but also provides several orders of speed up during inference. The idea is to hierarchically quantize the representation so that the quantization granularity is greatly increased while maintaining the accuracy and keeping the computational complexity low. We also show that the problem of finding the optimal sparse compound hash code respecting the hierarchical structure can be optimized in polynomial time via minimum cost flow in an equivalent flow network. This allows us to train the method end-to-end in a mini-batch stochastic gradient descent setting. Our experiments on Cifar100 and ImageNet datasets show the state of the art search accuracy while providing several orders of magnitude search speedup respectively over exhaustive linear search over the dataset.

Includes members:
  Member "efficient end to end learning for quantizable representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adapting grad cam for embedding networks")
  Member "deep supervised discrete hashing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep hashing methods")
  Member "visual discovery at pinterest":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "handling position bias for unbiased learning to rank in hotels search")
  Member "a downsampled variant of imagenet as an alternative to the cifar datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep metric learning via facility location":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "opengan open set generative adversarial networks")
  Member "a survey on learning to hash":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "hard aware deeply cascaded embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a metric learning reality check")
  Member "deep semantic ranking based hashing for multi label image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on deep hashing methods")

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.CV(Computer Vision and Pattern Recognition)