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: "sorting out typicality with the inverse moment matrix sos polynomial"
  Abstract: We study a surprising phenomenon related to the representation of a cloud of data points using polynomials. We start with the previously unnoticed empirical observation that, given a collection (a cloud) of data points, the sublevel sets of a certain distinguished polynomial capture the shape of the cloud very accurately. This distinguished polynomial is a sum-of-squares (SOS) derived in a simple manner from the inverse of the empirical moment matrix. In fact, this SOS polynomial is directly related to orthogonal polynomials and the Christoffel function. This allows to generalize and interpret extremality properties of orthogonal polynomials and to provide a mathematical rationale for the observed phenomenon. Among diverse potential applications, we illustrate the relevance of our results on a network intrusion detection task for which we obtain performances similar to existing dedicated methods reported in the literature.

Connected through hyperedge HE-1 (induced by source paper "homm higher order moment matching for unsupervised domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "selective transfer with reinforced transfer network for partial domain adaptation"
    - "towards self similarity consistency and feature discrimination for unsupervised domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "deep clustering for unsupervised learning of visual features"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "image to image translation for domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"

Connected through hyperedge HE-2 (induced by source paper "the empirical christoffel function with applications in data analysis"):
  with members:
    - "multiple instance learning christoffel function approach to distribution regression problem"

Connected through hyperedge HE-3 (induced by source paper "monet moments embedding network"):
  with members:
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "multi modal factorized bilinear pooling with co attention learning for visual question answering"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "a neural algorithm of artistic style"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "the empirical christoffel function with applications in machine learning"):
  with members:
    - "multiple instance learning christoffel function approach to distribution regression problem"

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.LG(Machine Learning)