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: "non metric space library manual"
  Abstract: This document describes a library for similarity searching. Even though the library contains a variety of metric-space access methods, our main focus is on search methods for non-metric spaces. Because there are fewer exact solutions for non-metric spaces, many of our methods give only approximate answers. Thus, the methods are evaluated in terms of efficiency-effectiveness trade-offs rather than merely in terms of their efficiency. Our goal is, therefore, to provide not only state-of-the-art approximate search methods for both non-metric and metric spaces, but also the tools to measure search quality. We concentrate on technical details, i.e., how to compile the code, run the benchmarks, evaluate results, and use our code in other applications. Additionally, we explain how to extend the code by adding new search methods and spaces.

Connected through hyperedge HE-1 (induced by source paper "production ranking systems a review"):
  with members:
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "practical constrained optimization of auction mechanisms in e commerce sponsored search advertising"
    - "eenmf an end to end neural matching framework for e commerce sponsored search"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "visually aware fashion recommendation and design with generative image models"
    - "pixie a system for recommending 3 billion items to 200 million users in real time"

Connected through hyperedge HE-2 (induced by source paper "nseen neural semantic embedding for entity normalization"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "biomedical named entity recognition via reference set augmented bootstrapping"
    - "a survey on recent advances in named entity recognition from deep learning models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "harp hierarchical representation learning for networks"
    - "analysis of the impact of negative sampling on link prediction in knowledge graphs"
    - "training region based object detectors with online hard example mining"

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.MS(Mathematical Software)