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 "shrewd semantic hierarchy based relational embeddings for weakly supervised deep hashing"):
  Abstract: Using class labels to represent class similarity is a typical approach to training deep hashing systems for retrieval; samples from the same or different classes take binary 1 or 0 similarity values. This similarity does not model the full rich knowledge of semantic relations that may be present between data points. In this work we build upon the idea of using semantic hierarchies to form distance metrics between all available sample labels; for example cat to dog has a smaller distance than cat to guitar. We combine this type of semantic distance into a loss function to promote similar distances between the deep neural network embeddings. We also introduce an empirical Kullback-Leibler divergence loss term to promote binarization and uniformity of the embeddings. We test the resulting SHREWD method and demonstrate improvements in hierarchical retrieval scores using compact, binary hash codes instead of real valued ones, and show that in a weakly supervised hashing setting we are able to learn competitively without explicitly relying on class labels, but instead on similarities between labels.

Includes members:
  Member "semantic hierarchy preserving deep hashing for large scale image retrieval":
    also connected through other hyperedges:
      (none)
  Member "hierarchy based image embeddings for semantic image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "dual asymmetric deep hashing learning":
    also connected through other hyperedges:
      (none)
  Member "mean local group average precision mlgap a new performance metric for hashing based retrieval":
    also connected through other hyperedges:
      (none)
  Member "label embedding network learning label representation for soft training of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchy based image embeddings for semantic image retrieval")
  Member "unsupervised semantic deep hashing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bio inspired hashing for unsupervised similarity search")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep hashing methods")
  Member "supervised deep hashing for hierarchical labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep cross modal hashing with hashing functions and unified hash codes jointly learning")

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.IR(Information Retrieval)