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: "a heterogeneous high dimensional approximate nearest neighbor algorithm"
  Abstract: We consider the problem of finding high dimensional approximate nearest neighbors. Suppose there are d independent rare features, each having its own independent statistics. A point x will have x_{i}=0 denote the absence of feature i, and x_{i}=1 its existence. Sparsity means that usually x_{i}=0. Distance between points is a variant of the Hamming distance. Dimensional reduction converts the sparse heterogeneous problem into a lower dimensional full homogeneous problem. However we will see that the converted problem can be much harder to solve than the original problem. Instead we suggest a direct approach. It consists of T tries. In try t we rearrange the coordinates in decreasing order of (1-r_{t,i})\frac{p_{i,11}}{p_{i,01}+p_{i,10}} \ln\frac{1}{p_{i,1*}} where 0<r_{t,i}<1 are uniform pseudo-random numbers, and the p's are the coordinate's statistical parameters. The points are lexicographically ordered, and each is compared to its neighbors in that order. #R##N#We analyze a generalization of this algorithm, show that it is optimal in some class of algorithms, and estimate the necessary number of tries to success. It is governed by an information like function, which we call bucketing forest information. Any doubts whether it is "information" are dispelled by another paper, where unrestricted bucketing information is defined.

Connected through hyperedge HE-1 (induced by source paper "tree wise distribution sensitive hashing efficient maximum likelihood classification by joint dimensionality reduction in known probabilistic settings"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"
    - "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"

Connected through hyperedge HE-2 (induced by source paper "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"):
  with members:
    - "lower bounds on locality sensitive 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.IT(Information Theory)