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 novel re weighting method for connectionist temporal classification"):
  Abstract: The connectionist temporal classification (CTC) enables end-to-end sequence learning by maximizing the probability of correctly recognizing sequences during training. With an extra $blank$ class, the CTC implicitly converts recognizing a sequence into classifying each timestep within the sequence. But the CTC loss is not intuitive for such classification task, so the class imbalance within each sequence, caused by the overwhelming $blank$ timesteps, is a knotty problem. In this paper, we define a piece-wise function as the pseudo ground-truth to reinterpret the CTC loss based on sequences as the cross entropy loss based on timesteps. The cross entropy form makes it easy to re-weight the CTC loss. Experiments on text recognition show that the weighted CTC loss solves the class imbalance problem as well as facilitates the convergence, generally leading to better results than the CTC loss. Beside this, the reinterpretation of CTC, as a brand new perspective, may be potentially useful in some other situations.

Includes members:
  Member "rosetta large scale system for text detection and recognition in images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multi modal sets")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "connectionist temporal modeling for weakly supervised action labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action graphs weakly supervised action localization with graph convolution networks")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "joint ctc attention based end to end speech recognition using multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speech corpus of ainu folklore and end to end speech recognition for ainu language")
  Member "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "reading scene text in deep convolutional sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scene text recognition via transformer")

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)