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: "fast incremental and personalized pagerank"
  Abstract: In this paper, we analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. We assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. #R##N#For global PageRank, we assume that the social network has $n$ nodes, and $m$ adversarially chosen edges arrive in a random order. We show that with a reset probability of $\epsilon$, the total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is $O(\frac{n\ln m}{\epsilon^{2}})$. This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs $\Omega(\frac{m^2}{\ln(1/(1-\epsilon))})$ total time and the Monte Carlo method needs $O(mn/\epsilon)$ total time; both are prohibitively expensive. Furthermore, we also show that we can handle deletions equally efficiently. #R##N#We then study the computation of the top $k$ personalized PageRanks starting from a seed node, assuming that personalized PageRanks follow a power-law with exponent $\alpha  q\ln n$ random walks starting from every node for large enough constant $q$ (using the approach outlined for global PageRank), then the expected number of calls made to the distributed social network database is $O(k/(R^{(1-\alpha)/\alpha}))$. #R##N#We also present experimental results from the social networking site, Twitter, verifying our assumptions and analyses. The overall result is that this algorithm is fast enough for real-time queries over a dynamic social network.

Connected through hyperedge HE-1 (induced by source paper "prince provider side interpretability with counterfactual explanations in recommender systems"):
  with members:
    - "jointly learning explainable rules for recommendation with knowledge graph"
    - "fairy a framework for understanding relationships between users actions and their social feeds"
    - "reinforcement knowledge graph reasoning for explainable recommendation"
    - "learning heterogeneous knowledge base embeddings for explainable recommendation"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "explainable reasoning over knowledge graphs for recommendation"
    - "explainable recommendation via multi task learning in opinionated text data"
    - "explainable recommendation a survey and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "random order models"):
  with members:
    - "robust algorithms for the secretary problem"
    - "prophet secretary for combinatorial auctions and matroids"
    - "combinatorial prophet inequalities"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "secretary problems with non uniform arrival order"
    - "o log log rank competitive ratio for the matroid secretary problem"
    - "prophet inequalities with limited information"
    - "primal beats dual on online packing lps in the random order model"

Connected through hyperedge HE-3 (induced by source paper "scalable graph embeddings via sparse transpose proximities"):
  with members:
    - "fast randomized pca for sparse data"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "graph embedding techniques applications and performance a survey"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "a survey on network embedding"
    - "fast warped graph embedding unifying framework and one click algorithm"

Connected through hyperedge HE-4 (induced by source paper "graphsac detecting anomalies in large scale graphs"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "learning structural node embeddings via diffusion wavelets"
    - "deep defense training dnns with improved adversarial robustness"
    - "towards robust detection of adversarial examples"
    - "semi supervised classification with graph convolutional networks"
    - "scalable anomaly ranking of attributed neighborhoods"
    - "heat kernel based community detection"
    - "pagerank beyond the web"

Connected through hyperedge HE-5 (induced by source paper "prsim sublinear time simrank computation on large power law graphs"):
  with members:
    - "probesim scalable single source and top k simrank computations on dynamic graphs"
    - "sling a near optimal index structure for simrank"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "approximate personalized pagerank on dynamic graphs"
    - "panther fast top k similarity search in large networks"
    - "algorithmic complexity of power law networks"
    - "efficient simrank computation via linearization"
    - "axiomatic ranking of network role similarity"

Connected through hyperedge HE-6 (induced by source paper "virtual web based personalized pagerank updating"):
  with members:
    - "bidirectional pagerank estimation from average case to worst case"
    - "fast ppr scaling personalized pagerank estimation for large graphs"
    - "pagerank beyond the web"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "data poisoning attack against unsupervised node embedding methods"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "realistic adversarial examples in 3d meshes"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-8 (induced by source paper "learning networks from random walk based node similarities"):
  with members:
    - "predicting positive and negative links with noisy queries theory practice"
    - "deepwalk online learning of social representations"
    - "near linear query complexity for graph inference"
    - "graph reconstruction via distance oracles"
    - "a tractable approach to finding closest truncated commute time neighbors in large graphs"
    - "hitting and commute times in large graphs are often misleading"

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)