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: "graph based time space trade offs for approximate near neighbors"
  Abstract: We take a first step towards a rigorous asymptotic analysis of graph-based approaches for finding (approximate) nearest neighbors in high-dimensional spaces, by analyzing the complexity of (randomized) greedy walks on the approximate near neighbor graph. For random data sets of size $n = 2^{o(d)}$ on the $d$-dimensional Euclidean unit sphere, using near neighbor graphs we can provably solve the approximate nearest neighbor problem with approximation factor $c > 1$ in query time $n^{\rho_q + o(1)}$ and space $n^{1 + \rho_s + o(1)}$, for arbitrary $\rho_q, \rho_s \geq 0$ satisfying \begin{align} (2c^2 - 1) \rho_q + 2 c^2 (c^2 - 1) \sqrt{\rho_s (1 - \rho_s)} \geq c^4. \end{align} Graph-based near neighbor searching is especially competitive with hash-based methods for small $c$ and near-linear memory, and in this regime the asymptotic scaling of a greedy graph-based search matches the recent optimal hash-based trade-offs of Andoni-Laarhoven-Razenshteyn-Waingarten [SODA'17]. We further study how the trade-offs scale when the data set is of size $n = 2^{\Theta(d)}$, and analyze asymptotic complexities when applying these results to lattice sieving.

Connected through hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"

Connected through hyperedge HE-2 (induced by source paper "towards similarity graphs constructed by deep reinforcement learning"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "the case for learned index structures"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "semi supervised classification with graph convolutional networks"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-3 (induced by source paper "graph based nearest neighbor search from practice to theory"):
  with members:
    - "graph based nearest neighbor search promises and failures"
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "optimal data dependent hashing for approximate near neighbors"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "lower bounds on locality sensitive hashing"

Connected through hyperedge HE-4 (induced by source paper "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "billion scale similarity search with gpus"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "hybrid lsh faster near neighbors reporting in high dimensional space"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "off the beaten path let s replace term based retrieval with k nn search"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-5 (induced by source paper "a continuation method for discrete optimization and its application to nearest neighbor classification"):
  with members:
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "breaking the curse of dimensionality with convex neural networks"
    - "fastfood approximate kernel expansions in loglinear time"

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)