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: "learning transformations for classification forests"
  Abstract: This work introduces a transformation-based learner model for classification forests. The weak learner at each split node plays a crucial role in a classification tree. We propose to optimize the splitting objective by learning a linear transformation on subspaces using nuclear norm as the optimization criteria. The learned linear transformation restores a low-rank structure for data from the same class, and, at the same time, maximizes the separation between different classes, thereby improving the performance of the split function. Theoretical and experimental results support the proposed framework.

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 "random forests can hash"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "learning transformations for clustering and classification"
    - "a general two step approach to learning based hashing"
    - "representation learning a review and new perspectives"
    - "self taught hashing for fast similarity search"

Connected through hyperedge HE-3 (induced by source paper "learning transformations for clustering and classification"):
  with members:
    - "robust subspace clustering"
    - "generalized principal component analysis gpca"
    - "communications inspired projection design with application to compressive sensing"
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "a geometric analysis of subspace clustering with outliers"
    - "learning efficient sparse and low rank models"
    - "robust principal component analysis"

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)