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: "sequentially generated instance dependent image representations for classification"
  Abstract: In this paper, we investigate a new framework for image classification that adaptively generates spatial representations. Our strategy is based on a sequential process that learns to explore the different regions of any image in order to infer its category. In particular, the choice of regions is specific to each image, directed by the actual content of previously selected regions.The capacity of the system to handle incomplete image information as well as its adaptive region selection allow the system to perform well in budgeted classification tasks by exploiting a dynamicly generated representation of each image. We demonstrate the system's abilities in a series of image-based exploration and classification tasks that highlight its learned exploration and inference abilities.

Connected through hyperedge HE-1 (induced by source paper "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"):
  with members:
    - "going deeper into first person activity recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "an active search strategy for efficient object class detection"
    - "very deep convolutional networks for large scale image recognition"
    - "a discriminative cnn video representation for event detection"
    - "finding action tubes"

Connected through hyperedge HE-2 (induced by source paper "describing multimedia content using attention based encoder decoder networks"):
  with members:
    - "end to end memory networks"
    - "language models for image captioning the quirks and what works"
    - "using descriptive video services to create a large data source for video annotation research"
    - "draw a recurrent neural network for image generation"
    - "attention based models for speech recognition"
    - "on using monolingual corpora in neural machine translation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "reinforced decision trees"):
  with members:
    - "draw a recurrent neural network for image generation"
    - "logarithmic time online multiclass prediction"
    - "distributed representations of sentences and documents"
    - "deep sequential neural network"

Connected through hyperedge HE-4 (induced by source paper "deep sequential neural network"):
  with members:
    - "recurrent models of visual attention"
    - "speech recognition with deep recurrent neural networks"
    - "text classification a sequential reading approach"

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)