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 "hwnet v2 an efficient word image representation for handwritten documents"):
  Abstract: We present a framework for learning efficient holistic representation for handwritten word images. The proposed method uses a deep convolutional neural network with traditional classification loss. The major strengths of our work lie in: (i) the efficient usage of synthetic data to pre-train a deep network, (ii) an adapted version of ResNet-34 architecture with region of interest pooling (referred as HWNet v2) which learns discriminative features with variable sized word images, and (iii) realistic augmentation of training data with multiple scales and elastic distortion which mimics the natural process of handwriting. We further investigate the process of fine-tuning at various layers to reduce the domain gap between synthetic and real domain and also analyze the in-variances learned at different layers using recent visualization techniques proposed in literature. #R##N#Our representation leads to state of the art word spotting performance on standard handwritten datasets and historical manuscripts in different languages with minimal representation size. On the challenging IAM dataset, our method is first to report an mAP above 0.90 for word spotting with a representation size of just 32 dimensions. Further more, we also present results on printed document datasets in English and Indic scripts which validates the generic nature of the proposed framework for learning word image representation.

Includes members:
  Member "neural word search in historical manuscript collections":
    also connected through other hyperedges:
      (none)
  Member "attribute cnns for word spotting in handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling")
  Member "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection")
  Member "phocnet a deep convolutional neural network for word spotting in handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "annotation free learning of deep representations for word spotting using synthetic data and self labeling")
  Member "matching handwritten document images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural word search in historical manuscript collections")
  Member "understanding neural networks through deep visualization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)