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: "fast exact max kernel search"
  Abstract: The wide applicability of kernels makes the problem of max-kernel search ubiquitous and more general than the usual similarity search in metric spaces. We focus on solving this problem efficiently. We begin by characterizing the inherent hardness of the max-kernel search problem with a novel notion of directional concentration. Following that, we present a method to use an $O(n \log n)$ algorithm to index any set of objects (points in $\Real^\dims$ or abstract objects) directly in the Hilbert space without any explicit feature representations of the objects in this space. We present the first provably $O(\log n)$ algorithm for exact max-kernel search using this index. Empirical results for a variety of data sets as well as abstract objects demonstrate up to 4 orders of magnitude speedup in some cases. Extensions for approximate max-kernel search are also presented.

Connected through hyperedge HE-1 (induced by source paper "index based high dimensional cosine threshold querying with optimality guarantees"):
  with members:
    - "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "on the complexity of inner product similarity join"
    - "cosine similarity search with multi index hashing"
    - "practical and optimal lsh for angular distance"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-2 (induced by source paper "to index or not to index optimizing exact maximum inner product search"):
  with members:
    - "clipper a low latency online prediction serving system"
    - "collaborative deep learning for recommender systems"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "simdex exploiting model similarity in exact matrix factorization recommendations"):
  with members:
    - "clipper a low latency online prediction serving system"
    - "collaborative deep learning for recommender systems"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "on the complexity of inner product similarity join"):
  with members:
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "i o efficient similarity join"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "sketching as a tool for numerical linear algebra"
    - "sketching and embedding are equivalent for norms"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

Connected through hyperedge HE-5 (induced by source paper "fast approximate furthest neighbors with data dependent hashing"):
  with members:
    - "plug and play dual tree algorithm runtime analysis"
    - "tree independent dual tree algorithms"
    - "mlpack a scalable c machine learning library"

Connected through hyperedge HE-6 (induced by source paper "dual tree k means with bounded iteration runtime"):
  with members:
    - "plug and play dual tree algorithm runtime analysis"
    - "tree independent dual tree algorithms"
    - "mlpack a scalable c machine learning library"

Connected through hyperedge HE-7 (induced by source paper "a faster subquadratic algorithm for finding outlier correlations"):
  with members:
    - "generating families of practical fast matrix multiplication algorithms"
    - "on the complexity of inner product similarity join"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "optimal data dependent hashing for approximate near neighbors"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "a framework for practical parallel fast matrix multiplication"

Connected through hyperedge HE-8 (induced by source paper "plug and play dual tree algorithm runtime analysis"):
  with members:
    - "tree independent dual tree algorithms"
    - "the anchors hierachy using the triangle inequality to survive high dimensional data"
    - "variational dual tree framework for large scale transition matrix approximation"
    - "mlpack a scalable c machine learning library"
    - "faster gaussian summation theory and experiment"

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.DS(Data Structures and Algorithms)