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 "hashing with binary matrix pursuit"):
  Abstract: We propose theoretical and empirical improvements for two-stage hashing methods. We first provide a theoretical analysis on the quality of the binary codes and show that, under mild assumptions, a residual learning scheme can construct binary codes that fit any neighborhood structure with arbitrary accuracy. Secondly, we show that with high-capacity hash functions such as CNNs, binary code inference can be greatly simplified for many standard neighborhood definitions, yielding smaller optimization problems and more robust codes. Incorporating our findings, we propose a novel two-stage hashing method that significantly outperforms previous hashing studies on widely used image retrieval benchmarks.

Includes members:
  Member "hashing with mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep hashing methods")
  Member "mihash online hashing with mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning with average precision training image retrieval with a listwise loss")
  Member "hashnet deep learning to hash by continuation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep hashing methods")
  Member "large scale binary quadratic optimization using semidefinite relaxation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization")
  Member "fast training of triplet based deep binary embedding networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep heterogeneous hashing for face video retrieval")
  Member "learning of generalized low rank models a greedy approach":
    also connected through other hyperedges:
      (none)
  Member "deep supervised hashing with triplet labels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on deep hashing methods")
  Member "binary hashing with semidefinite relaxation and augmented lagrangian":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on device scalable image based localization via prioritized cascade search and fast one many ransac")

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)