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: "computing the gromov hausdorff distance for metric trees"
  Abstract: The Gromov-Hausdorff (GH) distance is a natural way to measure distance between two metric spaces. We prove that it is $\mathrm{NP}$-hard to approximate the Gromov-Hausdorff distance better than a factor of $3$ for geodesic metrics on a pair of trees. We complement this result by providing a polynomial time $O(\min\{n, \sqrt{rn}\})$-approximation algorithm for computing the GH distance between a pair of metric trees, where $r$ is the ratio of the longest edge length in both trees to the shortest edge length. For metric trees with unit length edges, this yields an $O(\sqrt{n})$-approximation algorithm.

Connected through hyperedge HE-1 (induced by source paper "relative hausdorff distance for network analysis"):
  with members:
    - "multi centrality graph spectral decompositions and their application to cyber intrusion detection"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "graph based anomaly detection and description a survey"
    - "quantification and comparison of degree distributions in complex networks"
    - "information spreading in dynamic graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "the l cophenetic metric for phylogenetic trees as an interleaving distance"):
  with members:
    - "computational complexity of the interleaving distance"
    - "convergence between categorical representations of reeb space and mapper"
    - "categorified reeb graphs"
    - "binary adder circuits of asymptotically minimum depth linear size and fan out two"
    - "the edit distance for reeb graphs of surfaces"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-3 (induced by source paper "a structural average of labeled merge trees for uncertainty visualization"):
  with members:
    - "the l cophenetic metric for phylogenetic trees as an interleaving distance"
    - "intrinsic interleaving distance for merge trees"
    - "the topology toolkit"
    - "local equivalence and intrinsic metrics between reeb graphs"
    - "categorified reeb graphs"
    - "measuring distance between reeb graphs"

Connected through hyperedge HE-4 (induced by source paper "efficient estimation of a gromov hausdorff distance between unweighted graphs"):
  with members:
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-5 (induced by source paper "distance measures for embedded graphs"):
  with members:
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "subgraph isomorphism in planar graphs and related problems"

Connected through hyperedge HE-6 (induced by source paper "does the geometry of word embeddings help document classification a case study on persistent homology based representations"):
  with members:
    - "skip thought vectors"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-7 (induced by source paper "comparing graphs via persistence distortion"):
  with members:
    - "graph matching with anchor nodes a learning approach"
    - "graph isomorphism in quasipolynomial time"
    - "measuring distance between reeb graphs"

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)