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: "orientation covariant aggregation of local descriptors with embeddings"
  Abstract: Image search systems based on local descriptors typically achieve orientation invariance by aligning the patches on their dominant orientations. Albeit successful, this choice introduces too much invariance because it does not guarantee that the patches are rotated consistently. This paper introduces an aggregation strategy of local descriptors that achieves this covariance property by jointly encoding the angle in the aggregation stage in a continuous manner. It is combined with an efficient monomial embedding to provide a codebook-free method to aggregate local descriptors into a single vector representation. Our strategy is also compatible and employed with several popular encoding methods, in particular bag-of-words, VLAD and the Fisher vector. Our geometric-aware aggregation strategy is effective for image search, as shown by experiments performed on standard benchmarks for image and particular object retrieval, namely Holidays and Oxford buildings.

Connected through hyperedge HE-1 (induced by source paper "solar second order loss and attention for image retrieval"):
  with members:
    - "explicit spatial encoding for deep local descriptors"
    - "learning with average precision training image retrieval with a listwise loss"
    - "sosnet second order similarity regularization for local descriptor learning"
    - "detect to retrieve efficient regional aggregation for image search"
    - "non local neural networks"
    - "attention is all you need"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "working hard to know your neighbor s margins local descriptor learning loss"

Connected through hyperedge HE-2 (induced by source paper "aggregating binary local descriptors for image retrieval"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "latch learned arrangements of three patch codes"
    - "exploiting local features from deep networks for image retrieval"
    - "a practical guide to cnns and fisher vectors for image instance retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "neural codes for image retrieval"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-3 (induced by source paper "fine tuning cnn image retrieval with no human annotation"):
  with members:
    - "siamese network of deep fisher vector descriptors for image retrieval"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "nested invariance pooling and rbm hashing for image instance retrieval"
    - "large scale image retrieval with attentive deep local features"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

Connected through hyperedge HE-4 (induced by source paper "sift meets cnn a decade survey of instance retrieval"):
  with members:
    - "polysemous codes"
    - "faster r cnn features for instance search"
    - "lift learned invariant feature transform"
    - "end to end learning of deep visual representations for image retrieval"
    - "bags of local convolutional features for scalable instance search"
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "a survey on learning to hash"

Connected through hyperedge HE-5 (induced by source paper "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"):
  with members:
    - "deep image retrieval learning global representations for image search"
    - "good practice in cnn feature transfer"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "aggregating deep convolutional features for image retrieval"
    - "very deep convolutional networks for large scale image recognition"

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)