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: "sparse similarity preserving hashing"
  Abstract: In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing. One of the plights of existing hashing techniques is the intrinsic trade-off between performance and computational complexity: while longer hash codes allow for lower false positive rates, it is very difficult to increase the embedding dimensionality without incurring in very high false negatives rates or prohibiting computational costs. In this paper, we propose a way to overcome this limitation by enforcing the hash codes to be sparse. Sparse high-dimensional codes enjoy from the low false positive rates typical of long hashes, while keeping the false negative rates similar to those of a shorter dense hashing scheme with equal number of degrees of freedom. We use a tailored feed-forward neural network for the hashing function. Extensive experimental evaluation involving visual and multi-modal data shows the benefits of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "foresthash semantic hashing with shallow random forests and tiny convolutional networks"):
  with members:
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "striving for simplicity the all convolutional net"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning transformations for clustering and classification"

Connected through hyperedge HE-2 (induced by source paper "learning a deep ell_ infty encoder for hashing"):
  with members:
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "feature learning based deep supervised hashing with pairwise labels"

Connected through hyperedge HE-3 (induced by source paper "learning to hash for indexing big data a survey"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "compressing neural networks with the hashing trick"
    - "learning hash functions using column generation"
    - "inductive hashing on manifolds"
    - "a general two step approach to learning based hashing"
    - "on the difficulty of nearest neighbor search"
    - "compact hyperplane hashing with bilinear functions"

Connected through hyperedge HE-4 (induced by source paper "feature learning based deep supervised hashing with pairwise labels"):
  with members:
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"
    - "imagenet large scale visual recognition challenge"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning hash functions using column generation"

Connected through hyperedge HE-5 (induced by source paper "understanding locally competitive networks"):
  with members:
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "multimodal similarity preserving hashing"

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)