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: "generalized boundaries from multiple image interpretations"
  Abstract: Boundary detection is essential for a variety of computer vision tasks such as segmentation and recognition. In this paper we propose a unified formulation and a novel algorithm that are applicable to the detection of different types of boundaries, such as intensity edges, occlusion boundaries or object category specific boundaries. Our formulation leads to a simple method with state-of-the-art performance and significantly lower computational cost than existing methods. We evaluate our algorithm on different types of boundaries, from low-level boundaries extracted in natural images, to occlusion boundaries obtained using motion cues and RGB-D cameras, to boundaries from soft-segmentation. We also propose a novel method for figure/ground soft-segmentation that can be used in conjunction with our boundary detection method and improve its accuracy at almost no extra computational cost.

Connected through hyperedge HE-1 (induced by source paper "red net a recursive encoder decoder network for edge detection"):
  with members:
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "densely connected convolutional networks"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "deep hierarchical parsing for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "richer convolutional features for edge detection"):
  with members:
    - "deep crisp boundaries from boundaries to higher level tasks"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "webseg learning semantic segmentation from web searches"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "casenet deep category aware semantic edge detection"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "convolutional oriented boundaries"

Connected through hyperedge HE-3 (induced by source paper "contour detection from deep patch level boundary prediction"):
  with members:
    - "n 4 fields neural network nearest neighbor fields for image transforms"
    - "reconstructive sparse code transfer for contour detection and semantic labeling"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-4 (induced by source paper "semicontour a semi supervised learning approach for contour detection"):
  with members:
    - "holistically nested edge detection"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "n 4 fields neural network nearest neighbor fields for image transforms"
    - "reconstructive sparse code transfer for contour detection and semantic labeling"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-5 (induced by source paper "natural scene image segmentation based on multi layer feature extraction"):
  with members:
    - "image segmentation using hierarchical merge tree"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-6 (induced by source paper "segment phrase table for semantic segmentation visual entailment and paraphrasing"):
  with members:
    - "combining language and vision with a multimodal skip gram model"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"
    - "deep visual semantic alignments for generating image descriptions"
    - "show and tell a neural image caption generator"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-7 (induced by source paper "oriented edge forests for boundary detection"):
  with members:
    - "reconstructive sparse code transfer for contour detection and semantic labeling"
    - "fast edge detection using structured forests"

Connected through hyperedge HE-8 (induced by source paper "fast edge detection using structured forests"):
  with members:
    - "learning rich features from rgb d images for object detection and segmentation"

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)