human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "tablebank table benchmark for image based table detection and recognition"
  Abstract: We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousands human labeled examples, which is difficult to generalize on real world applications. With TableBank that contains 417K high-quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available (this https URL) and hope it will empower more deep learning approaches in the table detection and recognition task.

Connected through hyperedge HE-1 (induced by source paper "gfte graph based financial table extraction"):
  with members:
    - "complicated table structure recognition"
    - "publaynet largest dataset ever for document layout analysis"
    - "rethinking table recognition using graph neural networks"
    - "table understanding in structured documents"

Connected through hyperedge HE-2 (induced by source paper "the benefits of close domain fine tuning for table detection in document images"):
  with members:
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "ssd single shot multibox detector"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "imagenet large scale visual recognition challenge"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "complicated table structure recognition"):
  with members:
    - "a mixed hierarchical attention based encoder decoder approach for standard table summarization"
    - "attention is all you need"
    - "deep reinforcement learning for dialogue generation"
    - "compositional semantic parsing on semi structured tables"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)