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: "efficient distributed locality sensitive hashing"
  Abstract: Distributed frameworks are gaining increasingly widespread use in applications that process large amounts of data. One important example application is large scale similarity search, for which Locality Sensitive Hashing (LSH) has emerged as the method of choice, specially when the data is high-dimensional. At its core, LSH is based on hashing the data points to a number of buckets such that similar points are more likely to map to the same buckets. To guarantee high search quality, the LSH scheme needs a rather large number of hash tables. This entails a large space requirement, and in the distributed setting, with each query requiring a network call per hash bucket look up, this also entails a big network load. The Entropy LSH scheme proposed by Panigrahy significantly reduces the number of required hash tables by looking up a number of query offsets in addition to the query itself. While this improves the LSH space requirement, it does not help with (and in fact worsens) the search network efficiency, as now each query offset requires a network call. In this paper, focusing on the Euclidian space under $l_2$ norm and building up on Entropy LSH, we propose the distributed Layered LSH scheme, and prove that it exponentially decreases the network cost, while maintaining a good load balance between different machines. Our experiments also verify that our scheme results in a significant network traffic reduction that brings about large runtime improvement in real world applications.

Connected through hyperedge HE-1 (induced by source paper "on the i o complexity of the k nearest neighbor problem"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "hardness of approximate nearest neighbor search"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "neural codes for image retrieval"
    - "lower bounds on near neighbor search via metric expansion"
    - "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"
    - "dynamic external hashing the limit of buffering"
    - "indexing schemes for similarity search an illustrated paradigm"

Connected through hyperedge HE-2 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-3 (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-4 (induced by source paper "nearbucket lsh efficient similarity search in p2p networks"):
  with members:
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "distance queries from sampled data accurate and efficient"):
  with members:
    - "what you can do with coordinated samples"
    - "get the most out of your sample optimal unbiased estimators using partial information"
    - "coordinated weighted sampling for estimating aggregates over multiple weight assignments"
    - "leveraging discarded samples for tighter estimation of multiple set aggregates"
    - "stream sampling for variance optimal estimation of subset sums"

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.DC(Distributed, Parallel, and Cluster Computing)