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: "semantic decomposition and recognition of long and complex manipulation action sequences"
  Abstract: Understanding continuous human actions is a non-trivial but important problem in computer vision. Although there exists a large corpus of work in the recognition of action sequences, most approaches suffer from problems relating to vast variations in motions, action combinations, and scene contexts. In this paper, we introduce a novel method for semantic segmentation and recognition of long and complex manipulation action tasks, such as “preparing a breakfast” or “making a sandwich”. We represent manipulations with our recently introduced “Semantic Event Chain” (SEC) concept, which captures the underlying spatiotemporal structure of an action invariant to motion, velocity, and scene context. Solely based on the spatiotemporal interactions between manipulated objects and hands in the extracted SEC, the framework automatically parses individual manipulation streams performed either sequentially or concurrently. Using event chains, our method further extracts basic primitive elements of each parsed manipulation. Without requiring any prior object knowledge, the proposed framework can also extract object-like scene entities that exhibit the same role in semantically similar manipulations. We conduct extensive experiments on various recent datasets to validate the robustness of the framework.

Connected through hyperedge HE-1 (induced by source paper "scene understanding for autonomous manipulation with deep learning"):
  with members:
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "visual affordance and function understanding a survey"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "video captioning with transferred semantic attributes"
    - "weakly supervised learning of affordances"

Connected through hyperedge HE-2 (induced by source paper "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"):
  with members:
    - "object captioning and retrieval with natural language"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-3 (induced by source paper "learning actions from human demonstration video for robotic manipulation"):
  with members:
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "vision based multi task manipulation for inexpensive robots using end to end learning from demonstration"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "robotic grasp detection using deep convolutional neural networks"
    - "sequence to sequence video to text"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "action representations in robotics a taxonomy and systematic classification"):
  with members:
    - "seamless integration and coordination of cognitive skills in humanoid robots a deep learning approach"
    - "do what i want not what i did imitation of skills by planning sequences of actions"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"
    - "hand object interaction and precise localization in transitive action recognition"
    - "imagenet large scale visual recognition challenge"
    - "unstructured human activity detection from rgbd images"
    - "the symbol grounding problem"

Connected through hyperedge HE-5 (induced by source paper "translating videos to commands for robotic manipulation with deep recurrent neural networks"):
  with members:
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "top down visual saliency guided by captions"
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"
    - "video paragraph captioning using hierarchical recurrent neural networks"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)