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: "discriminative feature selection for uncertain graph classification"
  Abstract: Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, etc. Most measurement of interestingness of discriminative subgraph features are defined on certain graphs, where the structure of graph objects are certain, and the binary edges within each graph represent the "presence" of linkages among the nodes. In many real-world applications, however, the linkage structure of the graphs is inherently uncertain. Therefore, existing measurements of interestingness based upon certain graphs are unable to capture the structural uncertainty in these applications effectively. In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from conventional subgraph mining problems because both the structure of the graph objects and the discrimination score of each subgraph feature are uncertain. To address these challenges, we propose a novel discriminative subgraph feature selection method, DUG, which can find discriminative subgraph features in uncertain graphs based upon different statistical measures including expectation, median, mode and phi-probability. We first compute the probability distribution of the discrimination scores for each subgraph feature based on dynamic programming. Then a branch-and-bound algorithm is proposed to search for discriminative subgraphs efficiently. Extensive experiments on various neuroimaging applications (i.e., Alzheimer's Disease, ADHD and HIV) have been performed to analyze the gain in performance by taking into account structural uncertainties in identifying discriminative subgraph features for graph classification.

Connected through hyperedge HE-1 (induced by source paper "isonn isomorphic neural network for graph representation learning and classification"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "convolutional set matching for graph similarity"
    - "watch your step learning node embeddings via graph attention"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"
    - "node2vec scalable feature learning for networks"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-2 (induced by source paper "a review of heterogeneous data mining for brain disorders"):
  with members:
    - "multi view machines"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "mining brain networks using multiple side views for neurological disorder identification"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
    - "a survey on multi view learning"
    - "higher order partial least squares hopls a generalized multilinear regression method"

Connected through hyperedge HE-3 (induced by source paper "node classification in uncertain graphs"):
  with members:
    - "discriminative probabilistic models for relational data"
    - "uncertain time series similarity return to the basics"
    - "injecting uncertainty in graphs for identity obfuscation"

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)