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: "defining and evaluating network communities based on ground truth"
  Abstract: Nodes in real-world networks organize into densely linked communities where edges appear with high concentration among the members of the community. Identifying such communities of nodes has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable gold-standard ground-truth. #R##N#In this paper we study a set of 230 large real-world social, collaboration and information networks where nodes explicitly state their group memberships. For example, in social networks nodes explicitly join various interest based social groups. We use such groups to define a reliable and robust notion of ground-truth communities. We then propose a methodology which allows us to compare and quantitatively evaluate how different structural definitions of network communities correspond to ground-truth communities. We choose 13 commonly used structural definitions of network communities and examine their sensitivity, robustness and performance in identifying the ground-truth. We show that the 13 structural definitions are heavily correlated and naturally group into four classes. We find that two of these definitions, Conductance and Triad-participation-ratio, consistently give the best performance in identifying ground-truth communities. We also investigate a task of detecting communities given a single seed node. We extend the local spectral clustering algorithm into a heuristic parameter-free community detection method that easily scales to networks with more than hundred million nodes. The proposed method achieves 30% relative improvement over current local clustering methods.

Connected through hyperedge HE-1 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "certifiable robustness to graph perturbations"
    - "attacking graph based classification via manipulating the graph structure"

Connected through hyperedge HE-2 (induced by source paper "time constrained adaptive influence maximization"):
  with members:
    - "on adaptivity gaps of influence maximization under the independent cascade model with full adoption feedback"
    - "adaptive influence maximization with myopic feedback"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "adaptive influence maximization under general feedback models"
    - "on misinformation containment in online social networks"
    - "multi round influence maximization"
    - "adaptive submodular influence maximization with myopic feedback"
    - "fake news mitigation via point process based intervention"

Connected through hyperedge HE-3 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"

Connected through hyperedge HE-4 (induced by source paper "shapevis high dimensional data visualization at scale"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "statistical analysis and parameter selection for mapper"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "line large scale information network embedding"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-5 (induced by source paper "just slaq when you approximate accurate spectral distances for web scale graphs"):
  with members:
    - "network density of states"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "netlsd hearing the shape of a graph"
    - "regal representation learning based graph alignment"
    - "a simple yet effective baseline for non attributed graph classification"
    - "approximating the spectrum of a graph"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-6 (induced by source paper "graph clustering via qubo and digital annealing"):
  with members:
    - "using synthetic networks for parameter tuning in community detection"
    - "a statistical density based analysis of graph clustering algorithm performance"
    - "testing graph clusterability algorithms and lower bounds"
    - "a tutorial on formulating and using qubo models"
    - "axioms for graph clustering quality functions"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "2ps high quality edge partitioning with two phase streaming"):
  with members:
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "scalable edge partitioning"
    - "a streaming algorithm for graph clustering"
    - "adwise adaptive window based streaming edge partitioning for high speed graph processing"
    - "bubing massive crawling for the masses"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "optimizing ordered graph algorithms with graphit"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "distributionally linearizable data structures"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "gunrock gpu graph analytics"
    - "the gap benchmark suite"

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)