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: "coding for random projections"
  Abstract: The method of random projections has become very popular for large-scale applications in statistical learning, information retrieval, bio-informatics and other applications. Using a well-designed coding scheme for the projected data, which determines the number of bits needed for each projected value and how to allocate these bits, can significantly improve the effectiveness of the algorithm, in storage cost as well as computational speed. In this paper, we study a number of simple coding schemes, focusing on the task of similarity estimation and on an application to training linear classifiers. We demonstrate that uniform quantization outperforms the standard existing influential method (Datar et. al. 2004). Indeed, we argue that in many cases coding with just a small number of bits suffices. Furthermore, we also develop a non-uniform 2-bit coding scheme that generally performs well in practice, as confirmed by our experiments on training linear support vector machines (SVM).

Connected through hyperedge HE-1 (induced by source paper "locality sensitive hashing for f divergences mutual information loss and beyond"):
  with members:
    - "categorical feature compression via submodular optimization"
    - "norm ranging lsh for maximum inner product search"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "greedy merge degrading has optimal power law"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "f divergence inequalities"
    - "a survey on learning to hash"
    - "hashing for similarity search a survey"

Connected through hyperedge HE-2 (induced by source paper "sub linear privacy preserving near neighbor search"):
  with members:
    - "lower bounds for oblivious near neighbor search"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"
    - "a simple and practical algorithm for differentially private data release"

Connected through hyperedge HE-3 (induced by source paper "sub linear privacy preserving search with untrusted server and semi honest parties"):
  with members:
    - "riposte an anonymous messaging system handling millions of users"
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"):
  with members:
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"

Connected through hyperedge HE-5 (induced by source paper "hashing for similarity search a survey"):
  with members:
    - "optimized cartesian k means"
    - "fast supervised hashing with decision trees for high dimensional data"
    - "inner product similarity search using compositional codes"
    - "beyond locality sensitive hashing"
    - "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"

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.LG(Machine Learning)