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: "learning the semantic structure of objects from web supervision"
  Abstract: While recent research in image understanding has often focused on recognizing more types of objects, understanding more about the objects is just as important. Recognizing object parts and attributes has been extensively studied before, yet learning large space of such concepts remains elusive due to the high cost of providing detailed object annotations for supervision. The key contribution of this paper is an algorithm to learn the nameable parts of objects automatically, from images obtained by querying Web search engines. The key challenge is the high level of noise in the annotations; to address it, we propose a new unified embedding space where the appearance and geometry of objects and their semantic parts are represented uniformly. Geometric relationships are induced in a soft manner by a rich set of nonsemantic mid-level anchors, bridging the gap between semantic and non-semantic parts. We also show that the resulting embedding provides a visually-intuitive mechanism to navigate the learned concepts and their corresponding images.

Connected through hyperedge HE-1 (induced by source paper "learning semantic part based models from google images"):
  with members:
    - "webly supervised learning of convolutional networks"
    - "hypercolumns for object segmentation and fine grained localization"
    - "part based r cnns for fine grained category detection"
    - "semantic part segmentation using compositional model combining shape and appearance"
    - "detect what you can detecting and representing objects using holistic models and body parts"
    - "actions and attributes from wholes and parts"
    - "part detector discovery in deep convolutional neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "mining deep and or object structures via cost sensitive question answer based active annotations"):
  with members:
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "yolov3 an incremental improvement"
    - "network transplanting"
    - "interpreting cnn knowledge via an explanatory graph"
    - "mining object parts from cnns via active question answering"
    - "interpretable convolutional neural networks"

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)