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 "neural word search in historical manuscript collections"):
  Abstract: We address the problem of segmenting and retrieving word images in collections of historical manuscripts given a text query. This is commonly referred to as "word spotting". To this end, we first p ...

Includes members:
  Member "textboxes a single shot oriented scene text detector":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "towards end to end text spotting with convolutional recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "attribute cnns 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 "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection")
  Member "information theoretical label embeddings for large scale image classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale visual relationship understanding")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "phocnet a deep convolutional neural network for word spotting in handwritten documents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "exploring confidence measures for word spotting in heterogeneous datasets")

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)