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: "batch based activity recognition from egocentric photo streams"
  Abstract: Activity recognition from long unstructured egocentric photo-streams has several applications in assistive technology such as health monitoring and frailty detection, just to name a few. However, one of its main technical challenges is to deal with the low frame rate of wearable photo-cameras, which causes abrupt appearance changes between consecutive frames. In consequence, important discriminatory low-level features from motion such as optical flow cannot be estimated. In this paper, we present a batch-driven approach for training a deep learning architecture that strongly rely on Long short-term units to tackle this problem. We propose two different implementations of the same approach that process a photo-stream sequence using batches of fixed size with the goal of capturing the temporal evolution of high-level features. The main difference between these implementations is that one explicitly models consecutive batches by overlapping them. Experimental results over a public dataset acquired by three users demonstrate the validity of the proposed architectures to exploit the temporal evolution of convolutional features over time without relying on event boundaries.

Connected through hyperedge HE-1 (induced by source paper "batch based activity recognition from egocentric photo streams revisited"):
  with members:
    - "recognizing activities of daily living from egocentric images"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "going deeper into first person activity recognition"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "predicting daily activities from egocentric images using deep learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "on the role of event boundaries in egocentric activity recognition from photostreams"):
  with members:
    - "batch based activity recognition from egocentric photo streams revisited"
    - "who let the dogs out modeling dog behavior from visual data"
    - "recognizing activities of daily living from egocentric images"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "xception deep learning with depthwise separable convolutions"
    - "compact cnn for indexing egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"

Connected through hyperedge HE-3 (induced by source paper "egocentric video description based on temporally linked sequences"):
  with members:
    - "hierarchical lstm with adjusted temporal attention for video captioning"
    - "learning to remember rare events"
    - "dense captioning events in videos"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "attention based multimodal fusion for video description"
    - "video description using bidirectional recurrent neural networks"

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)