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 "learning to segment object candidates via recursive neural networks"):
  Abstract: To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for detecting object candidates over scales. Unlike traditional methods that mainly adopt fixed similarity measures for merging regions or finding object proposals, our approach adaptively learns the region merging similarity and the objectness measure during the process of hierarchical region grouping. Specifically, guided by a structured loss, the ReNN model jointly optimizes the cross-region similarity metric with the region merging process as well as the objectness prediction. During inference of the object proposal generation, we introduce randomness into the greedy search to cope with the ambiguity of grouping regions. Extensive experiments on standard benchmarks, e.g., PASCAL VOC and ImageNet, suggest that our approach is capable of producing object proposals with high recall while well preserving the object boundaries and outperforms other existing methods in both accuracy and efficiency.

Includes members:
  Member "knowledge embedded representation learning for fine grained image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization")
  Member "deep reasoning with knowledge graph for social relationship understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fashion retrieval via graph reasoning networks on a similarity pyramid")
  Member "active self paced learning for cost effective and progressive face identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "multi label image recognition by recurrently discovering attentional regions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pedestrian attribute recognition a survey")
  Member "cross domain visual matching via generalized similarity measure and feature learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised concatenation hashing via combining subspace learning and graph embedding for cross modal image retrieval")
  Member "recurrent attentional reinforcement learning for multi label image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tree structured policy based progressive reinforcement learning for temporally language grounding in video")
  Member "multiscale combinatorial grouping for image segmentation and object proposal generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "disc deep image saliency computing via progressive representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection")

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)