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: "a new embedding quality assessment method for manifold learning"
  Abstract: Manifold learning is a hot research topic in the field of computer science. A crucial issue with current manifold learning methods is that they lack a natural quantitative measure to assess the quality of learned embeddings, which greatly limits their applications to real-world problems. In this paper, a new embedding quality assessment method for manifold learning, named as Normalization Independent Embedding Quality Assessment (NIEQA), is proposed. Compared with current assessment methods which are limited to isometric embeddings, the NIEQA method has a much larger application range due to two features. First, it is based on a new measure which can effectively evaluate how well local neighborhood geometry is preserved under normalization, hence it can be applied to both isometric and normalized embeddings. Second, it can provide both local and global evaluations to output an overall assessment. Therefore, NIEQA can serve as a natural tool in model selection and evaluation tasks for manifold learning. Experimental results on benchmark data sets validate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "a review framework and r toolkit for exploring evaluating and comparing visualizations"):
  with members:
    - "a tutorial on principal component analysis"
    - "generalized principal component analysis gpca"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-2 (induced by source paper "state representation learning for control an overview"):
  with members:
    - "decoupling dynamics and reward for transfer learning"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "progressive neural networks"
    - "independently controllable factors"
    - "deep multimodal representation learning from temporal data"
    - "interactive perception leveraging action in perception and perception in action"

Connected through hyperedge HE-3 (induced by source paper "unsupervised state representation learning with robotic priors a robustness benchmark"):
  with members:
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "the curious robot learning visual representations via physical interactions"
    - "deep residual learning for image recognition"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep spatial autoencoders for visuomotor learning"
    - "learning to linearize under uncertainty"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "clustering is semidefinitely not that hard nonnegative sdp for manifold disentangling"):
  with members:
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "on the tightness of an sdp relaxation of k means"
    - "on semidefinite relaxations for the block model"
    - "a biclustering framework for consensus problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "algorithms for learning kernels based on centered alignment"

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)