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: "walk steered convolution for graph classification"
  Abstract: Graph classification is a fundamental but challenging problem due to the non-Euclidean property of graph. In this work, we jointly leverage the powerful representation ability of random walk and the essential success of standard convolutional network work (CNN), to propose a random walk based convolutional network, called walk-steered convolution (WSC). Different from those existing graph CNNs with deterministic neighbor searching, we randomly sample multi-scale walk fields by using random walk, which is more flexible to the scalability of graph. To encode each-scale walk field consisting of several walk paths, specifically, we characterize the directions of walk field by multiple Gaussian models so as to better analogize the standard CNNs on images. Each Gaussian implicitly defines a directions and all of them properly encode the spatial layout of walks after the gradient projecting to the space of Gaussian parameters. Further, a graph coarsening layer using dynamical clustering is stacked upon the Gaussian encoding to capture high-level semantics of graph. Comprehensive evaluations on several public datasets well demonstrate the superiority of our proposed graph learning method over other state-of-the-arts for graph classification.

Connected through hyperedge HE-1 (induced by source paper "context dependent diffusion network for visual relationship detection"):
  with members:
    - "tensor graph convolutional neural network"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "vip cnn visual phrase guided convolutional neural network"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"

Connected through hyperedge HE-2 (induced by source paper "open domain question answering using early fusion of knowledge bases and text"):
  with members:
    - "weaver deep co encoding of questions and documents for machine reading"
    - "the web as a knowledge base for answering complex questions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "reinforced mnemonic reader for machine comprehension"
    - "object oriented neural programming oonp for document understanding"
    - "improved neural relation detection for knowledge base question answering"
    - "question answering on knowledge bases and text using universal schema and memory networks"

Connected through hyperedge HE-3 (induced by source paper "gaussian induced convolution for graphs"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "tensor graph convolutional neural network"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "shift aggregate extract networks"
    - "spectral filter tracking"
    - "learning graph representations with embedding propagation"

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.SI(Social and Information Networks)