human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep learning for sensor based activity recognition a survey"):
  Abstract: Abstract   Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

Includes members:
  Member "balanced distribution adaptation for transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "impact of imagenet model selection on domain adaptation")
  Member "a deep learning framework using passive wifi sensing for respiration monitoring":
    also connected through other hyperedges:
      (none)
  Member "ensembles of deep lstm learners for activity recognition using wearables":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "are accelerometers for activity recognition a dead end")
  Member "transforming sensor data to the image domain for deep learning an application to footstep detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis")
  Member "sensor based gait parameter extraction with deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sensor fusion for predictive control of human prosthesis environment dynamics in assistive walking a survey")
  Member "deepsense a unified deep learning framework for time series mobile sensing data processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")
  Member "label free supervision of neural networks with physics and domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "deep convolutional and recurrent models for human activity recognition using wearables":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an active smartphone authentication method based on daily cyclical activity")

Question: Please tell me which class the source paper 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)