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: "finding endogenously formed communities"
  Abstract: A central problem in e-commerce is determining overlapping communities among individuals or objects in the absence of external identification or tagging. We address this problem by introducing a framework that captures the notion of communities or clusters determined by the relative affinities among their members. To this end we define what we call an affinity system, which is a set of elements, each with a vector characterizing its preference for all other elements in the set. We define a natural notion of (potentially overlapping) communities in an affinity system, in which the members of a given community collectively prefer each other to anyone else outside the community. Thus these communities are endogenously formed in the affinity system and are "self-determined" or "self-certified" by its members. #R##N#We provide a tight polynomial bound on the number of self-determined communities as a function of the robustness of the community. We present a polynomial-time algorithm for enumerating these communities. Moreover, we obtain a local algorithm with a strong stochastic performance guarantee that can find a community in time nearly linear in the of size the community. #R##N#Social networks fit particularly naturally within the affinity system framework -- if we can appropriately extract the affinities from the relatively sparse yet rich information from social networks, our analysis then yields a set of efficient algorithms for enumerating self-determined communities in social networks. In the context of social networks we also connect our analysis with results about $(\alpha,\beta)$-clusters introduced by Mishra, Schreiber, Stanton, and Tarjan \cite{msst}. In contrast with the polynomial bound we prove on the number of communities in the affinity system model, we show that there exists a family of networks with superpolynomial number of $(\alpha,\beta)$-clusters.

Connected through hyperedge HE-1 (induced by source paper "network essence pagerank completion and centrality conforming markov chains"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "network composition from multi layer data"
    - "interplay between social influence and network centrality a comparative study on shapley centrality and single node influence centrality"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "graphical models for game theory"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "efficient computation of the shapley value for game theoretic network centrality"

Connected through hyperedge HE-2 (induced by source paper "in a world that counts clustering and detecting fake social engagement at scale"):
  with members:
    - "overlapping community detection via local spectral clustering"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "network analysis of recurring youtube spam campaigns"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-3 (induced by source paper "detecting communities is hard and counting them is even harder"):
  with members:
    - "community recovery in graphs with locality"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "how robust are reconstruction thresholds for community detection"
    - "eth hardness for signaling in symmetric zero sum games"
    - "hardness results for signaling in bayesian zero sum and network routing games"
    - "mixture selection mechanism design and signaling"
    - "am with multiple merlins"
    - "axioms for graph clustering quality functions"

Connected through hyperedge HE-4 (induced by source paper "communities in preference networks refined axioms and beyond"):
  with members:
    - "fixed points of social choice an axiomatic approach to network communities"
    - "a uniqueness theorem for clustering"

Connected through hyperedge HE-5 (induced by source paper "eth hardness for densest k subgraph with perfect completeness"):
  with members:
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "combining traditional marketing and viral marketing with amphibious influence maximization"
    - "sum of squares lower bounds for planted clique"
    - "am with multiple merlins"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "finding overlapping communities in social networks toward a rigorous approach"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"

Connected through hyperedge HE-6 (induced by source paper "uncovering the small community structure in large networks a local spectral approach"):
  with members:
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "a markov random walk under constraint for discovering overlapping communities in complex networks"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-7 (induced by source paper "fixed points of social choice an axiomatic approach to network communities"):
  with members:
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "new algorithms for learning incoherent and overcomplete dictionaries"):
  with members:
    - "fourier pca and robust tensor decomposition"
    - "understanding alternating minimization for matrix completion"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "a spectral algorithm for latent dirichlet allocation"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
    - "recovering low rank matrices from few coefficients in any basis"

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)