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 overlapping communities in social networks toward a rigorous approach"
  Abstract: A "community" in a social network is usually understood to be a group of nodes more densely connected with each other than with the rest of the network. This is an important concept in most domains where networks arise: social, technological, biological, etc. For many years algorithms for finding communities implicitly assumed communities are nonoverlapping (leading to use of clustering-based approaches) but there is increasing interest in finding overlapping communities. A barrier to finding communities is that the solution concept is often defined in terms of an NP-complete problem such as Clique or Hierarchical Clustering. #R##N#This paper seeks to initiate a rigorous approach to the problem of finding overlapping communities, where "rigorous" means that we clearly state the following: (a) the object sought by our algorithm (b) the assumptions about the underlying network (c) the (worst-case) running time. #R##N#Our assumptions about the network lie between worst-case and average-case. An average case analysis would require a precise probabilistic model of the network, on which there is currently no consensus. However, some plausible assumptions about network parameters can be gleaned from a long body of work in the sociology community spanning five decades focusing on the study of individual communities and ego-centric networks. Thus our assumptions are somewhat "local" in nature. Nevertheless they suffice to permit a rigorous analysis of running time of algorithms that recover global structure. #R##N#Our algorithms use random sampling similar to that in property testing and algorithms for dense graphs. However, our networks are not necessarily dense graphs, not even in local neighborhoods. #R##N#Our algorithms explore a local-global relationship between ego-centric and socio-centric networks that we hope will provide a fruitful framework for future work both in computer science and sociology.

Connected through hyperedge HE-1 (induced by source paper "inapproximability of vc dimension and littlestone s dimension"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "on the approximability of partial vc dimension"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "eth hardness for densest k subgraph with perfect completeness"
    - "complexity theoretic limitations on learning halfspaces"
    - "eth hardness for signaling in symmetric zero sum games"

Connected through hyperedge HE-2 (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-3 (induced by source paper "don t be greedy leveraging community structure to find high quality seed sets for influence maximization"):
  with members:
    - "beyond worst case in approximability of nonsubmodular influence maximization"
    - "discovering small target sets in social networks a fast and effective algorithm"
    - "a cost function for similarity based hierarchical clustering"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "sketch based influence maximization and computation scaling up with guarantees"
    - "maximizing social influence in nearly optimal time"
    - "competitive contagion in networks"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-4 (induced by source paper "arbitrary overlap constraints in graph packing problems"):
  with members:
    - "overlapping communities in social networks"
    - "a classification for community discovery methods in complex networks"

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 endogenously formed communities"
    - "polynomial integrality gaps for strong sdp relaxations of densest k subgraph"

Connected through hyperedge HE-6 (induced by source paper "a community role approach to assess social capitalists visibility in the twitter network"):
  with members:
    - "identifying the community roles of social capitalists in the twitter network"

Connected through hyperedge HE-7 (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"
    - "finding endogenously formed communities"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "a tensor approach to learning mixed membership community models"):
  with members:
    - "dirichlet draws are sparse with high probability"
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "breaking the small cluster barrier of graph clustering"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "statistical algorithms and a lower bound for detecting planted clique"
    - "low distortion inference of latent similarities from a multiplex social network"

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)