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 "rgb d individual segmentation"):
  Abstract: Fine-grained recognition task deals with sub-category classification problem, which is important for real-world applications. In this work, we are particularly interested in the segmentation task on the \emph{finest-grained} level, which is specifically named "individual segmentation". In other words, the individual-level category has no sub-category under it. Segmentation problem in the individual level reveals some new properties, limited training data for single individual object, unknown background, and difficulty for the use of depth. To address these new problems, we propose a "Context Less-Aware" (CoLA) pipeline, which produces RGB-D object-predominated images that have less background context, and enables a scale-aware training and testing with 3D information. Extensive experiments show that the proposed CoLA strategy largely outperforms baseline methods on YCB-Video dataset and our proposed Supermarket-10K dataset. Code, trained model and new dataset will be published with this paper.

Includes members:
  Member "lf net learning local features from images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "srda generating instance segmentation annotation via scanning reasoning and domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "applying domain randomization to synthetic data for object category detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an annotation saved is an annotation earned using fully synthetic training for object instance detection")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "neighbourhood consensus networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "semi parametric image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object centric image generation from layouts")
  Member "path aggregation network for instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "sniper efficient multi scale training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)