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: "real time human pose estimation from video with convolutional neural networks"
  Abstract: In this paper, we present a method for real-time multi-person human pose estimation from video by utilizing convolutional neural networks. Our method is aimed for use case specific applications, where good accuracy is essential and variation of the background and poses is limited. This enables us to use a generic network architecture, which is both accurate and fast. We divide the problem into two phases: (1) pre-training and (2) finetuning. In pre-training, the network is learned with highly diverse input data from publicly available datasets, while in finetuning we train with application specific data, which we record with Kinect. Our method differs from most of the state-of-the-art methods in that we consider the whole system, including person detector, pose estimator and an automatic way to record application specific training material for finetuning. Our method is considerably faster than many of the state-of-the-art methods. Our method can be thought of as a replacement for Kinect, and it can be used for higher level tasks, such as gesture control, games, person tracking, action recognition and action tracking. We achieved accuracy of 96.8\% (PCK@0.2) with application specific data.

Connected through hyperedge HE-1 (induced by source paper "pose2instance harnessing keypoints for person instance segmentation"):
  with members:
    - "instance sensitive fully convolutional networks"
    - "bottom up instance segmentation using deep higher order crfs"
    - "chained predictions using convolutional neural networks"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "a multipath network for object detection"
    - "iterative instance segmentation"
    - "learning to decompose for object detection and instance segmentation"
    - "proposal free network for instance level object segmentation"

Connected through hyperedge HE-2 (induced by source paper "summarization of user generated sports video by using deep action recognition features"):
  with members:
    - "convolutional pose machines"
    - "video summarization with long short term memory"
    - "spatiotemporal residual networks for video action recognition"
    - "title generation for user generated videos"
    - "video summarization using deep semantic features"
    - "beyond short snippets deep networks for video classification"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "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)