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: "deepm a deep part based model for object detection and semantic part localization"
  Abstract: In this paper, we propose a deep part-based model (DeePM) for symbiotic object detection and semantic part localization. For this purpose, we annotate semantic parts for all 20 object categories on the PASCAL VOC 2012 dataset, which provides information on object pose, occlusion, viewpoint and functionality. DeePM is a latent graphical model based on the state-of-the-art R-CNN framework, which learns an explicit representation of the object-part configuration with flexible type sharing (e.g., a sideview horse head can be shared by a fully-visible sideview horse and a highly truncated sideview horse with head and neck only). For comparison, we also present an end-to-end Object-Part (OP) R-CNN which learns an implicit feature representation for jointly mapping an image ROI to the object and part bounding boxes. We evaluate the proposed methods for both the object and part detection performance on PASCAL VOC 2012, and show that DeePM consistently outperforms OP R-CNN in detecting objects and parts. In addition, it obtains superior performance to Fast and Faster R-CNNs in object detection.

Connected through hyperedge HE-1 (induced by source paper "deepvoting a robust and explainable deep network for semantic part detection under partial occlusion"):
  with members:
    - "visual concepts and compositional voting"
    - "single shot object detection with enriched semantics"
    - "scalenet guiding object proposal generation in supermarkets and beyond"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "you only look once unified real time object detection"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "modeling context in referring expressions"):
  with members:
    - "lstm a search space odyssey"
    - "ssd single shot multibox detector"
    - "grounding of textual phrases in images by reconstruction"
    - "generation and comprehension of unambiguous object descriptions"
    - "visalogy answering visual analogy questions"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "real time fine grained categorization with accuracy and interpretability"):
  with members:
    - "convolutional pose machines"
    - "deep deformation network for object landmark localization"
    - "fine grained pose prediction normalization and recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "object skeleton extraction in natural images by fusing scale associated deep side outputs"):
  with members:
    - "holistically nested edge detection"
    - "microsoft coco captions data collection and evaluation server"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "microsoft coco common objects in context"
    - "fast edge detection using structured forests"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "faster r cnn towards real time object detection with region proposal networks"):
  with members:
    - "what makes for effective detection proposals"
    - "object proposal evaluation protocol is gameable"
    - "learning to segment object candidates"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "instance aware semantic segmentation via multi task network cascades"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "part stacked cnn for fine grained visual categorization"):
  with members:
    - "weakly supervised fine grained categorization with part based image representation"
    - "bilinear cnns for fine grained visual recognition"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "part localization using multi proposal consensus for fine grained categorization"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "going deeper with convolutions"
    - "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)