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: "fingerprints fixed length representation via deep networks and domain knowledge"
  Abstract: We learn a discriminative fixed length feature representation of fingerprints which stands in contrast to commonly used unordered, variable length sets of minutiae points. To arrive at this fixed length representation, we embed fingerprint domain knowledge into a multitask deep convolutional neural network architecture. Empirical results, on two public-domain fingerprint databases (NIST SD4 and FVC 2004 DB1) show that compared to minutiae representations, extracted by two state-of-the-art commercial matchers (Verifinger v6.3 and Innovatrics v2.0.3), our fixed-length representations provide (i) higher search accuracy: Rank-1 accuracy of 97.9% vs. 97.3% on NIST SD4 against a gallery size of 2000 and (ii) significantly faster, large scale search: 682,594 matches per second vs. 22 matches per second for commercial matchers on an i5 3.3 GHz processor with 8 GB of RAM.

Connected through hyperedge HE-1 (induced by source paper "neural fuzzy extractors a secure way to use artificial neural networks for biometric user authentication"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "deep image retrieval learning global representations for image search"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep representations for iris face and fingerprint spoofing detection"
    - "very deep convolutional networks for large scale image recognition"
    - "low density lattice codes"

Connected through hyperedge HE-2 (induced by source paper "end to end pore extraction and matching in latent fingerprints going beyond minutiae"):
  with members:
    - "infant prints fingerprints for reducing infant mortality"
    - "automatic latent fingerprint segmentation"
    - "end to end latent fingerprint search"
    - "automatic dataset annotation to learn cnn pore description for fingerprint recognition"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"

Connected through hyperedge HE-3 (induced by source paper "infant prints fingerprints for reducing infant mortality"):
  with members:
    - "fingerprint match in box"
    - "raspireader open source fingerprint reader"

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)