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: "on the geometry of similarity search dimensionality curse and concentration of measure"
  Abstract: We suggest that the curse of dimensionality affecting the similarity-based search in large datasets is a manifestation of the phenomenon of concentration of measure on high-dimensional structures. We prove that, under certain geometric assumptions on the query domain $\Omega$ and the dataset $X$, if $\Omega$ satisfies the so-called concentration property, then for most query points $x^\ast$ the ball of radius $(1+\e)d_X(x^\ast)$ centred at $x^\ast$ contains either all points of $X$ or else at least $C_1\exp(-C_2\e^2n)$ of them. Here $d_X(x^\ast)$ is the distance from $x^\ast$ to the nearest neighbour in $X$ and $n$ is the dimension of $\Omega$.

Connected through hyperedge HE-1 (induced by source paper "universal bayes consistency in metric spaces"):
  with members:
    - "a new lower bound for agnostic learning with sample compression schemes"
    - "nearest neighbor sample compression efficiency consistency infinite dimensions"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"

Connected through hyperedge HE-2 (induced by source paper "intrinsic dimension of concept lattices"):
  with members:
    - "intrinsic dimension of a dataset what properties does one expect"
    - "an axiomatic approach to intrinsic dimension of a dataset"

Connected through hyperedge HE-3 (induced by source paper "nearest neighbor sample compression efficiency consistency infinite dimensions"):
  with members:
    - "active nearest neighbor learning in metric spaces"
    - "maximum margin multiclass nearest neighbors"
    - "rates of convergence for nearest neighbor classification"
    - "a bayes consistent 1 nn classifier"
    - "adaptive metric dimensionality reduction"

Connected through hyperedge HE-4 (induced by source paper "indexability concentration and vc theory"):
  with members:
    - "curse of dimensionality in pivot based indexes"
    - "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "curse of dimensionality in the application of pivot based indexes to the similarity search problem"):
  with members:
    - "intrinsic dimension of a dataset what properties does one expect"

Connected through hyperedge HE-6 (induced by source paper "curse of dimensionality in pivot based indexes"):
  with members:
    - "an axiomatic approach to intrinsic dimension of a dataset"

Connected through hyperedge HE-7 (induced by source paper "lower bounds on performance of metric tree indexing schemes for exact similarity search in high dimensions"):
  with members:
    - "indexability concentration and vc theory"
    - "lower bounds on near neighbor search via metric expansion"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "intrinsic dimension of a dataset what properties does one expect"):
  with members:
    - "what is the dimension of your binary data"
    - "indexing schemes for similarity search an illustrated paradigm"
    - "a geometric framework for modelling similarity search"

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.IR(Information Retrieval)