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: "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"
  Abstract: Dramatic appearance variation due to pose constitutes a great challenge in fine-grained recognition, one which recent methods using attention mechanisms or second-order statistics fail to adequately address. Modern CNNs typically lack an explicit understanding of object pose and are instead confused by entangled pose and appearance. In this paper, we propose a unified object representation built from a hierarchy of pose-aligned regions. Rather than representing an object by regions aligned to image axes, the proposed representation characterizes appearance relative to the object's pose using pose-aligned patches whose features are robust to variations in pose, scale and rotation. We propose an algorithm that performs pose estimation and forms the unified object representation as the concatenation of hierarchical pose-aligned regions features, which is then fed into a classification network. The proposed algorithm surpasses the performance of other approaches, increasing the state-of-the-art by nearly 2% on the widely-used CUB-200 dataset and by more than 8% on the much larger NABirds dataset. The effectiveness of this paradigm relative to competing methods suggests the critical importance of disentangling pose and appearance for continued progress in fine-grained recognition.

Connected through hyperedge HE-1 (induced by source paper "learning landmark guided embeddings for animal re identification"):
  with members:
    - "robust re identification of manta rays from natural markings by learning pose invariant embeddings"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "amur tiger re identification in the wild"
    - "spatial temporal person re identification"
    - "survey on deep learning techniques for person re identification task"
    - "in defense of the triplet loss for person re identification"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "pose driven deep convolutional model for person re identification"

Connected through hyperedge HE-2 (induced by source paper "combinational class activation maps for weakly supervised object localization"):
  with members:
    - "residual non local attention networks for image restoration"
    - "semantic matching by weakly supervised 2d point set registration"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "long term feature banks for detailed video understanding"
    - "non local netvlad encoding for video classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "non locally enhanced encoder decoder network for single image de raining"

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)