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: "on the computation of complex valued gradients with application to statistically optimum beamforming"
  Abstract: This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic operations the derivative of the eigenvalue problem with complex-valued eigenvectors is one of the key results of this report. The potential of this approach is shown with experimental results on the CHiME-3 challenge database. There, the beamforming task is used as a front-end for an ASR system. With the developed derivatives a joint optimization of a speech enhancement and speech recognition system w.r.t. the recognition optimization criterion is possible.

Connected through hyperedge HE-1 (induced by source paper "unsupervised training of neural mask based beamforming"):
  with members:
    - "unsupervised training of a deep clustering model for multichannel blind source separation"
    - "building state of the art distant speech recognition using the chime 4 challenge with a setup of speech enhancement baseline"
    - "unsupervised deep clustering for source separation direct learning from mixtures using spatial information"
    - "bootstrapping single channel source separation via unsupervised spatial clustering on stereo mixtures"
    - "tensorflow a system for large scale machine learning"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-2 (induced by source paper "dcfnet discriminant correlation filters network for visual tracking"):
  with members:
    - "fully convolutional siamese networks for object tracking"
    - "ssd single shot multibox detector"
    - "learning to track at 100 fps with deep regression networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "understanding and diagnosing visual tracking systems"
    - "deep residual learning for image recognition"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "staple complementary learners for real time tracking"

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.NA(Numerical Analysis)