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: "the shortlist method for fast computation of the earth mover s distance and finding optimal solutions to transportation problems"
  Abstract: Finding solutions to the classical transportation problem is of great importance, since this optimization problem arises in many engineering and computer science applications. Especially the Earth Mover's Distance is used in a plethora of applications ranging from content-based image retrieval, shape matching, fingerprint recognition, object tracking and phishing web page detection to computing color differences in linguistics and biology. Our starting point is the well-known revised simplex algorithm, which iteratively improves a feasible solution to optimality. The Shortlist Method that we propose substantially reduces the number of candidates inspected for improving the solution, while at the same time balancing the number of pivots required. Tests on simulated benchmarks demonstrate a considerable reduction in computation time for the new method as compared to the usual revised simplex algorithm implemented with state-of-the-art initialization and pivot strategies. As a consequence, the Shortlist Method facilitates the computation of large scale transportation problems in viable time. In addition we describe a novel method for finding an initial feasible solution which we coin Modified Russell's Method.

Connected through hyperedge HE-1 (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"
    - "efficient estimation of word representations in vector space"
    - "indexing the earth mover s distance using normal distributions"
    - "planar earthmover is not in l_1"

Connected through hyperedge HE-2 (induced by source paper "filter design and performance evaluation for fingerprint image segmentation"):
  with members:
    - "directional global three part image decomposition"
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"
    - "modeling the growth of fingerprints improves matching for adolescents"
    - "construction of hilbert transform pairs of wavelet bases and gabor like transforms"

Connected through hyperedge HE-3 (induced by source paper "directional global three part image decomposition"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "global variational method for fingerprint segmentation by three part decomposition"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "imaging with kantorovich rubinstein discrepancy"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-4 (induced by source paper "global variational method for fingerprint segmentation by three part decomposition"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"
    - "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-5 (induced by source paper "the filament sensor for near real time detection of cytoskeletal fiber structures"):
  with members:
    - "a multi orientation analysis approach to retinal vessel tracking"
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"

Connected through hyperedge HE-6 (induced by source paper "skilled impostor attacks against fingerprint verification systems and its remedy"):
  with members:
    - "filter design and performance evaluation for fingerprint image segmentation"

Connected through hyperedge HE-7 (induced by source paper "separating the real from the synthetic minutiae histograms as fingerprints of fingerprints"):
  with members:
    - "curved region based ridge frequency estimation and curved gabor filters for fingerprint image enhancement"
    - "modeling the growth of fingerprints improves matching for adolescents"

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.CV(Computer Vision and Pattern Recognition)