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: "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"
  Abstract: Within a mathematically rigorous model, we analyse the curse of dimensionality for deterministic exact similarity search in the context of popular indexing schemes: metric trees. The datasets $X$ are sampled randomly from a domain $\Omega$, equipped with a distance, $\rho$, and an underlying probability distribution, $\mu$. While performing an asymptotic analysis, we send the intrinsic dimension $d$ of $\Omega$ to infinity, and assume that the size of a dataset, $n$, grows superpolynomially yet subexponentially in $d$. Exact similarity search refers to finding the nearest neighbour in the dataset $X$ to a query point $\omega\in\Omega$, where the query points are subject to the same probability distribution $\mu$ as datapoints. Let $\mathscr F$ denote a class of all 1-Lipschitz functions on $\Omega$ that can be used as decision functions in constructing a hierarchical metric tree indexing scheme. Suppose the VC dimension of the class of all sets $\{\omega\colon f(\omega)\geq a\}$, $a\in\R$ is $o(n^{1/4}/\log^2n)$. (In view of a 1995 result of Goldberg and Jerrum, even a stronger complexity assumption $d^{O(1)}$ is reasonable.) We deduce the $\Omega(n^{1/4})$ lower bound on the expected average case performance of hierarchical metric-tree based indexing schemes for exact similarity search in $(\Omega,X)$. In paricular, this bound is superpolynomial in $d$.

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"
    - "efficient distributed locality sensitive hashing"
    - "lower bounds on near neighbor search via metric expansion"
    - "dynamic external hashing the limit of buffering"
    - "indexing schemes for similarity search an illustrated paradigm"

Connected through hyperedge HE-2 (induced by source paper "off the beaten path let s replace term based retrieval with k nn search"):
  with members:
    - "using centroids of word embeddings and word mover s distance for biomedical document retrieval in question answering"
    - "practical and optimal lsh for angular distance"
    - "permutation search methods are efficient yet faster search is possible"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "retrofitting word vectors to semantic lexicons"
    - "hashing for similarity search a survey"
    - "fast neighborhood graph search using cartesian concatenation"

Connected through hyperedge HE-3 (induced by source paper "on projection based operators in lp space for exact similarity search"):
  with members:
    - "indexability concentration and vc theory"

Connected through hyperedge HE-4 (induced by source paper "indexability concentration and vc theory"):
  with members:
    - "curse of dimensionality in pivot based indexes"
    - "intrinsic dimension of a dataset what properties does one expect"
    - "an axiomatic approach to intrinsic dimension of a dataset"
    - "indexing schemes for similarity search in datasets of short protein fragments"
    - "indexing schemes for similarity search an illustrated paradigm"
    - "on the geometry of similarity search dimensionality curse and concentration of measure"

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)