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: "interpretable convolutional neural networks via feedforward design"
  Abstract: The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP as a reference. The FF design adopts a data-centric approach. It derives network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner. To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform. It is a variant of the principal component analysis (PCA) with an added bias vector to annihilate activation's nonlinearity. Multiple Saab transforms in cascade yield multiple convolutional layers. As to fully-connected (FC) layers, we construct them using a cascade of multi-stage linear least squared regressors (LSRs). The classification and robustness (against adversarial attacks) performances of BP- and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are compared. Finally, we comment on the relationship between BP and FF designs.

Connected through hyperedge HE-1 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models"):
  with members:
    - "are sixteen heads really better than one"
    - "language models as knowledge bases"
    - "pitfalls in the evaluation of sentence embeddings"
    - "evaluating word embedding models methods and experimental results"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "albert a lite bert for self supervised learning of language representations"
    - "how contextual are contextualized word representations comparing the geometry of bert elmo and gpt 2 embeddings"
    - "revealing the dark secrets of bert"

Connected through hyperedge HE-2 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "orthogonal deep neural networks"
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-3 (induced by source paper "pointhop a lightweight learning model on point sets for 3d classification"):
  with members:
    - "pointhop an explainable machine learning method for point cloud classification"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "on data driven saak transform"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "foldingnet point cloud auto encoder via deep grid deformation"

Connected through hyperedge HE-4 (induced by source paper "graph representation learning a survey"):
  with members:
    - "learning graph pooling and hybrid convolutional operations for text representations"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "graph based deep tree recursive neural network dtrnn for text classification"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "hypergraph neural networks"
    - "mile a multi level framework for scalable graph embedding"
    - "attention models in graphs a survey"

Connected through hyperedge HE-5 (induced by source paper "pointhop an explainable machine learning method for point cloud classification"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "ensembles of feedforward designed convolutional neural networks"
    - "pointpillars fast encoders for object detection from point clouds"
    - "meshnet mesh neural network for 3d shape representation"
    - "dynamic graph cnn for learning on point clouds"
    - "pvnet a joint convolutional network of point cloud and multi view for 3d shape recognition"
    - "pointnetvlad deep point cloud based retrieval for large scale place recognition"
    - "on data driven saak transform"

Connected through hyperedge HE-6 (induced by source paper "tsxplain demystification of dnn decisions for time series using natural language and statistical features"):
  with members:
    - "tsviz demystification of deep learning models for time series analysis"
    - "textual explanations for self driving vehicles"
    - "neural network interpretation via fine grained textual summarization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "forecasting across time series databases using recurrent neural networks on groups of similar series a clustering approach"
    - "generating visual explanations"
    - "understanding neural networks through deep visualization"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning via feedforward designed convolutional neural networks"):
  with members:
    - "ensembles of feedforward designed convolutional neural networks"
    - "on data driven saak transform"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "good semi supervised learning that requires a bad gan"
    - "interpretable convolutional neural networks"
    - "improved techniques for training gans"
    - "understanding convolutional neural networks with a mathematical model"

Connected through hyperedge HE-8 (induced by source paper "ensembles of feedforward designed convolutional neural networks"):
  with members:
    - "on data driven saak transform"
    - "a saak transform approach to efficient scalable and robust handwritten digits recognition"
    - "interpretable convolutional neural networks"
    - "understanding convolutional neural networks with a mathematical model"
    - "deep residual learning for image recognition"

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)