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 "a targeted acceleration and compression framework for low bit neural networks"):
  Abstract: 1 bit deep neural networks (DNNs), of which both the activations and weights are binarized , are attracting more and more attention due to their high computational efficiency and low memory requirement . However, the drawback of large accuracy dropping also restrict s its application. In this paper, we propose a novel Targeted Acceleration and Compression (TAC) framework to improve the performance of 1 bit deep neural networks W e consider that the acceleration and compression effects of binarizing fully connected layer s are not sufficient to compensate for the accuracy loss caused by it In the proposed framework, t he convolutional and fully connected layer are separated and optimized i ndividually . F or the convolutional layer s , both the activations and weights are binarized. For the fully connected layer s, the binarization operation is re placed by network pruning and low bit quantization. The proposed framework is implemented on the CIFAR 10, CIFAR 100 and ImageNet ( ILSVRC 12 ) datasets , and experimental results show that the proposed TAC can significantly improve the accuracy of 1 bit deep neural networks and outperforms the state of the art by more than 6 percentage points .

Includes members:
  Member "3 d personvlad learning deep global representations for video based person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "cycle consistent deep generative hashing for cross modal retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "what and where to match deep spatially multiplicative integration networks for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "from hashing to cnns training binaryweight networks via hashing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bringing giant neural networks down to earth with unlabeled data")
  Member "multiview spectral clustering via structured low rank matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attribute guided feature learning network for vehicle re identification")
  Member "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning architectures for binary networks")
  Member "effective multi query expansions collaborative deep networks for robust landmark retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discriminative feature and dictionary learning with part aware model for vehicle re identification")
  Member "towards accurate binary convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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)