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: "planar earthmover is not in l_1"
  Abstract: We show that any $L_1$ embedding of the transportation cost (a.k.a. Earthmover) metric on probability measures supported on the grid $\{0,1,...,n\}^2\subseteq \R^2$ incurs distortion $\Omega(\sqrt{\log n})$. We also use Fourier analytic techniques to construct a simple $L_1$ embedding of this space which has distortion $O(\log n)$.

Connected through hyperedge HE-1 (induced by source paper "scalable nearest neighbor search for optimal transport"):
  with members:
    - "hierarchical optimal transport for document representation"
    - "scalable fair clustering"
    - "approximate nearest neighbor search in high dimensions"
    - "practical data dependent metric compression with provable guarantees"
    - "sketching and embedding are equivalent for norms"
    - "distributed representations of words and phrases and their compositionality"
    - "ramsey partitions and proximity data structures"

Connected through hyperedge HE-2 (induced by source paper "approximate nearest neighbor search in high dimensions"):
  with members:
    - "optimal las vegas locality sensitive data structures"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "learning to hash for indexing big data a survey"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "locality sensitive hashing without false negatives"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "hashing for similarity search a survey"
    - "sparser johnson lindenstrauss transforms"

Connected through hyperedge HE-3 (induced by source paper "nonlinear dimension reduction via outer bi lipschitz extensions"):
  with members:
    - "optimal terminal dimensionality reduction in euclidean space"
    - "optimality of the johnson lindenstrauss lemma"
    - "approximate near neighbors for general symmetric norms"
    - "prioritized metric structures and embedding"
    - "sketching and embedding are equivalent for norms"
    - "constant factor approximation for balanced cut in the pie model"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "metric extension operators vertex sparsifiers and lipschitz extendability"

Connected through hyperedge HE-4 (induced by source paper "low complexity data parallel earth mover s distance approximations"):
  with members:
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "linear complexity relaxed word mover s distance with gpu acceleration"
    - "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"
    - "efficient estimation of word representations in vector space"
    - "indexing the earth mover s distance using normal distributions"

Connected through hyperedge HE-5 (induced by source paper "on the definiteness of earth mover s distance and its relation to set intersection"):
  with members:
    - "sliced wasserstein kernels for probability distributions"

Connected through hyperedge HE-6 (induced by source paper "approximate near neighbors for general symmetric norms"):
  with members:
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "learning to hash for indexing big data a survey"
    - "approximate nearest neighbor search for ell_p spaces 2 p infty via embeddings"
    - "streaming symmetric norms via measure concentration"
    - "optimal data dependent hashing for approximate near neighbors"
    - "sketching and embedding are equivalent for norms"
    - "hashing for similarity search a survey"
    - "a directed isoperimetric inequality with application to bregman near neighbor lower bounds"

Connected through hyperedge HE-7 (induced by source paper "nearly optimal classification for semimetrics"):
  with members:
    - "near optimal sample compression for nearest neighbors"
    - "maximum margin multiclass nearest neighbors"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-8 (induced by source paper "sketching and embedding are equivalent for norms"):
  with members:
    - "streaming symmetric norms via measure concentration"
    - "sketching as a tool for numerical linear algebra"
    - "an optimal lower bound on the communication complexity of gap hamming distance"
    - "a multi round communication lower bound for gap hamming and some consequences"
    - "optimal lower bounds for locality sensitive hashing except when q is tiny"
    - "better gap hamming lower bounds via better round elimination"
    - "lower bounds on locality sensitive hashing"

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.CG(Computational Geometry)