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: "bucketing coding and information theory for the statistical high dimensional nearest neighbor problem"
  Abstract: The problem of finding high-dimensional approximate nearest neighbors is considered when the data is generated by some known probabilistic model. A large natural class of algorithms (bucketing codes) is investigated, Bucketing information is defined, and is proven to bound the performance of all bucketing codes. The bucketing information bound is asymptotically attained by some randomly constructed bucketing codes. The example of n Bernoulli(1/2) very long (length d → ∞) sequences of bits is singled out. It is assumed that n - 2m sequences are completely independent, while the remaining 2m sequences are composed of m dependent pairs. The interdependence within each pair is that their bits agree with probability 1/2   0. A specific 2-D inequality (proven in another paper) implies that the exponent 1/p cannot be lowered. Moreover, if one sequence out of each pair belongs to a known set of n(2p-1)2 sequences, pairing can be done using order n1+∈ comparisons!

Connected through hyperedge HE-1 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"

Connected through hyperedge HE-2 (induced by source paper "polytopes lattices and spherical codes for the nearest neighbor problem"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "hypercube lsh for approximate near neighbors"
    - "fast cross polytope locality sensitive hashing"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-3 (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"
    - "a heterogeneous high dimensional approximate nearest neighbor algorithm"

Connected through hyperedge HE-4 (induced by source paper "a new algorithm for finding closest pair of vectors"):
  with members:
    - "an illuminating algorithm for the light bulb problem"
    - "fast feasible and unfeasible matrix multiplication"
    - "distributed pcp theorems for hardness of approximation in p"
    - "generating families of practical fast matrix multiplication algorithms"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "a faster subquadratic algorithm for finding outlier correlations"
    - "probabilistic polynomials and hamming nearest neighbors"

Connected through hyperedge HE-5 (induced by source paper "an illuminating algorithm for the light bulb problem"):
  with members:
    - "polynomial representations of threshold functions and algorithmic applications"
    - "probabilistic polynomials and hamming nearest neighbors"
    - "powers of tensors and fast matrix multiplication"
    - "natural proofs versus derandomization"

Connected through hyperedge HE-6 (induced by source paper "optimal las vegas locality sensitive data structures"):
  with members:
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "locality sensitive hashing without false negatives for l_p"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "distance sensitive bloom filters without false negatives"
    - "scalability and total recall with fast coveringlsh"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-7 (induced by source paper "fast locality sensitive hashing frameworks for approximate near neighbor search"):
  with members:
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-8 (induced by source paper "hypercube lsh for approximate near neighbors"):
  with members:
    - "fast cross polytope locality sensitive hashing"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"

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)