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: "deephash getting regularization depth and fine tuning right"
  Abstract: This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations -- regularization, depth and fine-tuning -- each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20 percent over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features -- a remarkable 512 times compression.

Connected through hyperedge HE-1 (induced by source paper "robust angular local descriptor learning"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "sampling matters in deep embedding learning"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning deep embeddings with histogram loss"
    - "wxbs wide baseline stereo generalizations"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "geodesc learning local descriptors by integrating geometry constraints"):
  with members:
    - "a large dataset for improving patch matching"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "no fuss distance metric learning using proxies"
    - "learning spread out local feature descriptors"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "learning local descriptors by optimizing the keypoint correspondence criterion"

Connected through hyperedge HE-3 (induced by source paper "learning spread out local feature descriptors"):
  with members:
    - "no fuss distance metric learning using proxies"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "lift learned invariant feature transform"
    - "learning deep embeddings with histogram loss"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised triplet hashing for fast image retrieval"):
  with members:
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "cnn based hashing for image retrieval"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "localizing and orienting street views using overhead imagery"):
  with members:
    - "planet photo geolocation with convolutional neural networks"
    - "sketch based 3d shape retrieval using convolutional neural networks"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "wide area image geolocalization with aerial reference imagery"
    - "learning to see by moving"

Connected through hyperedge HE-6 (induced by source paper "learning deep embeddings with histogram loss"):
  with members:
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep metric learning via lifted structured feature embedding"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "deep learning face representation by joint identification verification"
    - "deep metric learning for practical person re identification"

Connected through hyperedge HE-7 (induced by source paper "group invariant deep representations for image instance retrieval"):
  with members:
    - "tiny descriptors for image retrieval with unsupervised triplet hashing"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "a baseline for visual instance retrieval with deep convolutional networks"
    - "a deep representation for invariance and music classification"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deeppose human pose estimation via deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "co regularized deep representations for video summarization"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "visualizing and understanding convolutional networks"

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)