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: "amur tiger re identification in the wild"
  Abstract: Monitoring the population and movements of endangered species is an important task to wildlife conversation. Traditional tagging methods do not scale to large populations, while applying computer vision methods to camera sensor data requires re-identification (re-ID) algorithms to obtain accurate counts and moving trajectory of wildlife. However, existing re-ID methods are largely targeted at persons and cars, which have limited pose variations and constrained capture environments. This paper tries to fill the gap by introducing a novel large-scale dataset, the Amur Tiger Re-identification in the Wild (ATRW) dataset. ATRW contains over 8,000 video clips from 92 Amur tigers, with bounding box, pose keypoint, and tiger identity annotations. In contrast to typical re-ID datasets, the tigers are captured in a diverse set of unconstrained poses and lighting conditions. We demonstrate with a set of baseline algorithms that ATRW is a challenging dataset for re-ID. Lastly, we propose a novel method for tiger re-identification, which introduces precise pose parts modeling in deep neural networks to handle large pose variation of tigers, and reaches notable performance improvement over existing re-ID methods. The dataset will be public available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "unsupervised part based disentangling of object shape and appearance"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"

Connected through hyperedge HE-2 (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"
    - "spatial temporal person re identification"
    - "survey on deep learning techniques for person re identification task"
    - "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"
    - "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-3 (induced by source paper "learning from synthetic animals"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "confidence regularized self training"
    - "multi adversarial faster rcnn for unrestricted object detection"
    - "a robust learning approach to domain adaptive object detection"
    - "nlnl negative learning for noisy labels"
    - "automatic adaptation of object detectors to new domains using self training"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "diversify and match a domain adaptive representation learning paradigm for object detection"

Connected through hyperedge HE-4 (induced by source paper "fast and efficient model for real time tiger detection in the wild"):
  with members:
    - "blazeface sub millisecond neural face detection on mobile gpus"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "fd mobilenet improved mobilenet with a fast downsampling strategy"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ssd single shot multibox detector"
    - "xception deep learning with depthwise separable convolutions"
    - "adam a method for stochastic optimization"

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)