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 "visual concept recognition and localization via iterative introspection"):
  Abstract: Convolutional neural networks have been shown to develop internal representations, which correspond closely to semantically meaningful objects and parts, although trained solely on class labels. Class Activation Mapping (CAM) is a recent method that makes it possible to easily highlight the image regions contributing to a network's classification decision. We build upon these two developments to enable a network to re-examine informative image regions, which we term introspection. We propose a weakly-supervised iterative scheme, which shifts its center of attention to increasingly discriminative regions as it progresses, by alternating stages of classification and introspection. We evaluate our method and show its effectiveness over a range of several datasets, where we obtain competitive or state-of-the-art results: on Stanford-40 Actions, we set a new state-of the art of 81.74%. On FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements over baselines, some of which include significantly more supervision.

Includes members:
  Member "weakly supervised fine grained categorization with part based image representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "fine grained pose prediction normalization and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "bilinear cnn models for fine grained visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "deep spatial pyramid the devil is once again in the details":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns")
  Member "neural activation constellations unsupervised part model discovery with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpretable cnns for object classification")
  Member "the unreasonable effectiveness of noisy data for fine grained recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cocopie making mobile ai sweet as pie compression compilation co design goes a long way")
  Member "active object localization with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")

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)