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: "quasi metrics similarities and searches aspects of geometry of protein datasets"
  Abstract: A quasi-metric is a distance function which satisfies the triangle inequality but is not symmetric: it can be thought of as an asymmetric metric. The central result of this thesis, developed in Chapter 3, is that a natural correspondence exists between similarity measures between biological (nucleotide or protein) sequences and quasi-metrics. #R##N#Chapter 2 presents basic concepts of the theory of quasi-metric spaces and introduces a new examples of them: the universal countable rational quasi-metric space and its bicompletion, the universal bicomplete separable quasi-metric space. Chapter 4 is dedicated to development of a notion of the quasi-metric space with Borel probability measure, or pq-space. The main result of this chapter indicates that `a high dimensional quasi-metric space is close to being a metric space'. #R##N#Chapter 5 investigates the geometric aspects of the theory of database similarity search in the context of quasi-metrics. The results about $pq$-spaces are used to produce novel theoretical bounds on performance of indexing schemes. #R##N#Finally, the thesis presents some biological applications. Chapter 6 introduces FSIndex, an indexing scheme that significantly accelerates similarity searches of short protein fragment datasets. Chapter 7 presents the prototype of the system for discovery of short functional protein motifs called PFMFind, which relies on FSIndex for similarity searches.

Connected through hyperedge HE-1 (induced by source paper "classification in asymmetric spaces via sample compression"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "maximum margin multiclass nearest neighbors"
    - "efficient classification for metric data"
    - "fast construction of nets in low dimensional metrics and their applications"

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)