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 "semi supervised hierarchical semantic object parsing"):
  Abstract: Models based on Convolutional Neural Networks (CNNs) have been proven very successful for semantic segmentation and object parsing that yield hierarchies of features. Our key insight is to build convolutional networks that take input of arbitrary size and produce object parsing output with efficient inference and learning. In this work, we focus on the task of instance segmentation and parsing which recognizes and localizes objects down to a pixel level base on deep CNN. Therefore, unlike some related work, a pixel cannot belong to multiple instances and parsing. Our model is based on a deep neural network trained for object masking that supervised with input image and follow incorporates a Conditional Random Field (CRF) with end-to-end trainable piecewise order potentials based on object parsing outputs. In each CRF unit we designed terms to capture the short range and long range dependencies from various neighbors. The accurate instance-level segmentation that our network produce is reflected by the considerable improvements obtained over previous work at high APr thresholds. We demonstrate the effectiveness of our model with extensive experiments on challenging dataset subset of PASCAL VOC2012.

Includes members:
  Member "look into person self supervised structure sensitive learning and a new benchmark for human parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "icnet for real time semantic segmentation on high resolution images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "semantic object parsing with graph lstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "pyramid scene parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "fully convolutional instance aware semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "instance aware semantic segmentation via multi task network cascades":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "semantic object parsing with local global long short term memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical human parsing with typed part relation reasoning")

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.AI(Artificial Intelligence)