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 "face attribute prediction using off the shelf deep learning networks"):
  Abstract: Attribute prediction from face images in the wild is a challenging problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial feature extraction, and classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed by using deep learning networks. Current state-of-the-art performance was achieved by using two cascaded CNNs, which were specifically trained to learn face localization and facial attribute prediction. In this paper we experiment in an alternative way of exploring the power of deep representations from the networks: we employ off-the-shelf CNNs trained for face recognition tasks to do facial feature extraction, combined with conventional face localization techniques. Recognizing that the describable face attributes are diverse, we select representations from different levels of the CNNs and investigate their utilities for attribute classification. Experiments on two large datasets, LFWA and CeleA, show that the performance is totally comparable to state-of-the-art approach. Our findings suggest two potentially important questions in using CNNs for face attribute prediction: 1) How to maximally leverage the power of CNN representations. 2) How to best combine traditional computer vision techniques with deep learning networks.

Includes members:
  Member "deepid3 face recognition with very deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the megaface benchmark 1 million faces for recognition at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "hd cnn hierarchical deep convolutional neural network for large scale visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers")
  Member "deep visual semantic alignments for generating image descriptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "cnn features off the shelf an astounding baseline for recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")

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)