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 "an implicit attention mechanism for deep learning pedestrian re identification frameworks"):
  Abstract: Attention is defined as the preparedness for the mental selection of certain aspects in a physical environment. In the computer vision domain, this mechanism is of most interest, as it helps to define the segments of an image/video that are critical for obtaining a specific decision. This paper introduces one 'implicit' attentional mechanism for deep learning frameworks, that provides simultaneously: 1) masks-free; and 2) foreground-focused samples for the inference phase. The main idea is to generate synthetic data composed of interleaved segments from the original learning set, while using class information only from specific segments. During the learning phase, the newly generated samples feed the network, keeping their label exclusively consistent with the identity from where the region-of-interest was cropped. Hence, as the model receives images of each identity with inconsistent unwanted areas, it naturally pays the most attention to the label consistent consistent regions, which we observed to be equivalent to learn an effective receptive field. During the test phase, samples are provided without any mask, and the network naturally disregards the detrimental information, which is the insight for the observed improvements in performance. As a proof-of-concept, we consider the challenging problem of pedestrian re-identification and compare the effectiveness of our solution to the state-of-the-art techniques in the well known Richly Annotated Pedestrian (RAP) dataset. The code is available at this https URL.

Includes members:
  Member "spherereid deep hypersphere manifold embedding for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "bag of tricks and a strong baseline for deep person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "triplet based deep similarity learning for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abd net attentive but diverse person re identification")
  Member "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "data augmentation by pairing samples for images classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "human semantic parsing for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical human parsing with typed part relation reasoning")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "a discriminatively learned cnn embedding for person reidentification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification")

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.LG(Machine Learning)