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: "entropy based nearest neighbor search in high dimensions"
  Abstract: In this paper we study the problem of finding the approximate nearest neighbor of a query point in the high dimensional space, focusing on the Euclidean space. The earlier approaches use locality-preserving hash functions (that tend to map nearby points to the same value) to construct several hash tables to ensure that the query point hashes to the same bucket as its nearest neighbor in at least one table. Our approach is different -- we use one (or a few) hash table and hash several randomly chosen points in the neighborhood of the query point showing that at least one of them will hash to the bucket containing its nearest neighbor. We show that the number of randomly chosen points in the neighborhood of the query point $q$ required depends on the entropy of the hash value $h(p)$ of a random point $p$ at the same distance from $q$ at its nearest neighbor, given $q$ and the locality preserving hash function $h$ chosen randomly from the hash family. Precisely, we show that if the entropy $I(h(p)|q,h) = M$ and $g$ is a bound on the probability that two far-off points will hash to the same bucket, then we can find the approximate nearest neighbor in $O(n^\rho)$ time and near linear $\tilde O(n)$ space where $\rho = M/\log(1/g)$. Alternatively we can build a data structure of size $\tilde O(n^{1/(1-\rho)})$ to answer queries in $\tilde O(d)$ time. By applying this analysis to the locality preserving hash functions in and adjusting the parameters we show that the $c$ nearest neighbor can be computed in time $\tilde O(n^\rho)$ and near linear space where $\rho \approx 2.06/c$ as $c$ becomes large.

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 "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-3 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "fast similarity sketching"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"

Connected through hyperedge HE-4 (induced by source paper "lower bounds for oblivious near neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "hardness of approximate nearest neighbor search"
    - "scalable secure computation of statistical functions with applications to k nearest neighbors"
    - "lower bounds for oblivious data structures"
    - "distributed pcp theorems for hardness of approximation in p"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "new unconditional hardness results for dynamic and online problems"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"

Connected through hyperedge HE-5 (induced by source paper "nearest neighbor decoding for tardos fingerprinting codes"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "optimal sequential fingerprinting wald vs tardos"
    - "practical and optimal lsh for angular distance"
    - "locality sensitive hashing without false negatives"
    - "optimal symmetric tardos traitor tracing schemes"
    - "capacities and capacity achieving decoders for various fingerprinting games"
    - "enhanced blind decoding of tardos codes with new map based functions"

Connected through hyperedge HE-6 (induced by source paper "approximate nearest neighbor search in high dimensions"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "learning to hash for indexing big data a survey"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "locality sensitive hashing without false negatives"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "hashing for similarity search a survey"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-7 (induced by source paper "confirmation sampling for exact nearest neighbor search"):
  with members:
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "scalable and robust set similarity join"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "practical and optimal lsh for angular distance"
    - "optimal data dependent hashing for approximate near neighbors"

Connected through hyperedge HE-8 (induced by source paper "learning to fingerprint the latent structure in question articulation"):
  with members:
    - "language use matters analysis of the linguistic structure of question texts can characterize answerability in quora"
    - "squad 100 000 questions for machine comprehension of text"
    - "adam a method for stochastic optimization"

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)