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: "column networks for collective classification"
  Abstract: Relational learning deals with data that are characterized by relational structures. An important task is collective classification, which is to jointly classify networked objects. While it holds a great promise to produce a better accuracy than non-collective classifiers, collective classification is computational challenging and has not leveraged on the recent breakthroughs of deep learning. We present Column Network (CLN), a novel deep learning model for collective classification in multi-relational domains. CLN has many desirable theoretical properties: (i) it encodes multi-relations between any two instances; (ii) it is deep and compact, allowing complex functions to be approximated at the network level with a small set of free parameters; (iii) local and relational features are learned simultaneously; (iv) long-range, higher-order dependencies between instances are supported naturally; and (v) crucially, learning and inference are efficient, linear in the size of the network and the number of relations. We evaluate CLN on multiple real-world applications: (a) delay prediction in software projects, (b) PubMed Diabetes publication classification and (c) film genre classification. In all applications, CLN demonstrates a higher accuracy than state-of-the-art rivals.

Connected through hyperedge HE-1 (induced by source paper "non parametric learning of lifted restricted boltzmann machines"):
  with members:
    - "structure learning for relational logistic regression an ensemble approach"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning to compare relation network for few shot learning"
    - "learning convolutional neural networks for graphs"
    - "complex embeddings for simple link prediction"
    - "holographic embeddings of knowledge graphs"
    - "lifted relational neural networks"

Connected through hyperedge HE-2 (induced by source paper "deepgcns making gcns go as deep as cnns"):
  with members:
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"
    - "graph neural networks a review of methods and applications"
    - "partnet a large scale benchmark for fine grained and hierarchical part level 3d object understanding"
    - "graph convolutional neural networks for web scale recommender systems"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "hypergraph convolution and hypergraph attention"):
  with members:
    - "link prediction based on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-4 (induced by source paper "diffusion improves graph learning"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "node embedding with adaptive similarities for scalable learning over graphs"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-5 (induced by source paper "deepgcns can gcns go as deep as cnns"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph r cnn for scene graph generation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "semi supervised user geolocation via graph convolutional networks"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-6 (induced by source paper "graph neural networks with convolutional arma filters"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "on the transferability of spectral graph filters"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "pine universal deep embedding for graph nodes via partial permutation invariant set functions"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "universal approximations of invariant maps by neural networks"
    - "learning structural node embeddings via diffusion wavelets"
    - "deep sets"
    - "graph embedding techniques applications and performance a survey"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "pu gcn point cloud upsampling using graph convolutional networks"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "self attention graph pooling"
    - "pu gan a point cloud upsampling adversarial network"
    - "deepgcns can gcns go as deep as cnns"
    - "deepgcns making gcns go as deep as cnns"
    - "hierarchical graph representation learning with differentiable pooling"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "dynamic graph cnn for learning on point clouds"

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)