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: "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
  Abstract: A large body of work has been devoted to defining and identifying clusters or communities in social and information networks. We explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. We employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the "best" possible community--according to the conductance measure--over a wide range of size scales. We study over 100 large real-world social and information networks. Our results suggest a significantly more refined picture of community structure in large networks than has been appreciated previously. In particular, we observe tight communities that are barely connected to the rest of the network at very small size scales; and communities of larger size scales gradually "blend into" the expander-like core of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, it is exactly the opposite of what one would expect based on intuition from expander graphs, low-dimensional or manifold-like graphs, and from small social networks that have served as testbeds of community detection algorithms. We have found that a generative graph model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community profile plot similar to what we observe in our network datasets.

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 "influence maximization in unknown social networks learning policies for effective graph sampling"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "learning structural node embeddings via diffusion wavelets"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "prioritized experience replay"
    - "deepwalk online learning of social representations"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "irie scalable and robust influence maximization in social networks"

Connected through hyperedge HE-3 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures"):
  with members:
    - "blind inference of centrality rankings from graph signals"
    - "towards quantifying sampling bias in network inference"
    - "sampling from social networks with attributes"
    - "node2vec scalable feature learning for networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-4 (induced by source paper "node immunization with non backtracking eigenvalues"):
  with members:
    - "graph comparison via the nonbacktracking spectrum"
    - "assessing percolation threshold based on high order non backtracking matrices"
    - "a scalable generative graph model with community structure"
    - "localization and centrality in networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-5 (induced by source paper "local hypergraph clustering using capacity releasing diffusion"):
  with members:
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "flow based algorithms for local graph clustering"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"

Connected through hyperedge HE-6 (induced by source paper "computing the k densest subgraphs of a graph"):
  with members:
    - "top k overlapping densest subgraphs approximation and complexity"
    - "density friendly graph decomposition"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-7 (induced by source paper "optimizing generalized pagerank methods for seed expansion community detection"):
  with members:
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "quadratic decomposable submodular function minimization theory and practice"
    - "diffusion improves graph learning"
    - "mean field analysis of personalized pagerank with implications for local graph clustering"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "finding cheeger cuts in hypergraphs via heat equation"
    - "block models and personalized pagerank"
    - "snap a general purpose network analysis and graph mining library"

Connected through hyperedge HE-8 (induced by source paper "is a single embedding enough learning node representations that capture multiple social contexts"):
  with members:
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "enhanced network embeddings via exploiting edge labels"
    - "a tutorial on network embeddings"
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"
    - "harp hierarchical representation learning for networks"
    - "learning edge representations via low rank asymmetric projections"
    - "watch your step learning node embeddings via graph attention"

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.DS(Data Structures and Algorithms)