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: "segmental spatio temporal cnns for fine grained action segmentation and classification"
  Abstract: Joint segmentation and classification of fine-grained actions is important for applications in human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. In this paper, we propose a new spatio-temporal CNN model for fine-grained action classification and segmentation, which combines (1) a spatial CNN to represent objects in the scene and their spatial relationships; (2) a temporal CNN that captures how object relationships within an action change over time; and (3) a semi-Markov model that captures transitions from one action to another. In addition, we introduce an efficient segmental inference algorithm for joint segmentation and classification of actions that is orders of magnitude faster than state-of-the-art approaches. We highlight the effectiveness of our approach on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.

Connected through hyperedge HE-1 (induced by source paper "novel evaluation of surgical activity recognition models using task based efficiency metrics"):
  with members:
    - "future state predicting lstm for early surgery type recognition"
    - "joint surgical gesture and task classification with multi task and multimodal learning"
    - "surgical activity recognition in robot assisted radical prostatectomy using deep learning"
    - "recognizing surgical activities with recurrent neural networks"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "segmental spatiotemporal cnns for fine grained action segmentation"

Connected through hyperedge HE-2 (induced by source paper "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video"):
  with members:
    - "sembed semantic embedding of egocentric action videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "connectionist temporal modeling for weakly supervised action labeling"
    - "going deeper into first person activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "pooled motion features for first person videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)