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: "on the difficulty of nearest neighbor search"
  Abstract: Fast approximate nearest neighbor (NN) search in large databases is becoming popular. Several powerful learning-based formulations have been proposed recently. However, not much attention has been paid to a more fundamental question: how difficult is (approximate) nearest neighbor search in a given data set? And which data properties affect the difficulty of nearest neighbor search and how? This paper introduces the first concrete measure called Relative Contrast that can be used to evaluate the influence of several crucial data characteristics such as dimensionality, sparsity, and database size simultaneously in arbitrary normed metric spaces. Moreover, we present a theoretical analysis to prove how the difficulty measure (relative contrast) determines/affects the complexity of Local Sensitive Hashing, a popular approximate NN search method. Relative contrast also provides an explanation for a family of heuristic hashing algorithms with good practical performance based on PCA. Finally, we show that most of the previous works in measuring NN search meaningfulness/difficulty can be derived as special asymptotic cases for dense vectors of the proposed measure.

Connected through hyperedge HE-1 (induced by source paper "efficient task specific data valuation for nearest neighbor algorithms"):
  with members:
    - "data markets to support ai for all pricing valuation and governance"
    - "towards efficient data valuation based on the shapley value"
    - "model based pricing for machine learning in a data marketplace"
    - "databright towards a global exchange for decentralized data ownership and trusted computation"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "binary adaptive embeddings from order statistics of random projections"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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"
    - "sparse similarity preserving hashing"
    - "compact hyperplane hashing with bilinear functions"

Connected through hyperedge HE-4 (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"
    - "coding for random projections"
    - "a general two step approach to learning based 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.LG(Machine Learning)