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: "the densest k subhypergraph problem"
  Abstract: The Densest k-Subgraph (DkS) problem, and its corresponding minimization problem Smallest p-Edge Subgraph (SpES), have come to play a central role in approximation algorithms. This is due both to their practical importance, and their usefulness as a tool for solving and establishing approximation bounds for other problems. These two problems are not well understood, and it is widely believed that they do not an admit a subpolynomial approximation ratio (although the best known hardness results do not rule this out). In this paper we generalize both DkS and SpES from graphs to hypergraphs. We consider the Densest k-Subhypergraph problem (given a hypergraph (V,E), find a subset W ⊆ V of k vertices so as to maximize the number of hyperedges contained in W ) and define the Minimum p-Union problem (given a hypergraph, choose p of the hyperedges so as to minimize the number of vertices in their union). We focus in particular on the case where all hyperedges have size 3, as this is the simplest non-graph setting. For this case we provide an O(n4(4− √ 3)/13+ ) ≤ O(n )-approximation (for arbitrary constant > 0) for Densest k-Subhypergraph and an O(n)-approximation for Minimum p-Union. We also give an O( √ m)-approximation for Minimum p-Union in general hypergraphs. Finally, we examine the interesting special case of interval hypergraphs (instances where the vertices are a subset of the natural numbers and the hyperedges are intervals of the line) and prove that both problems admit an exact polynomial time solution on these instances. ∗Department of Computer Science, Ben Gurion University. Partially supported by ISF grant 1002/14. Email: chlamtac@cs.bgu.ac.il. †Department of Computer Science, Johns Hopkins University. Partially supported by NSF grants 1464239 and 1535887. Email: mdinitz@cs.jhu.edu ‡ICE-TCS, School of Computer Science, Reykjavik University. Supported by Icelandic Research Fund grants 120032011 and 152679-051. Email: christiank@ru.is. §Computer Science Department, Rutgers University, Camden, NY, USA. Partially supported by NSF grants 1218620 and 1540547. Email: guyk@crab.rutgers.edu ¶Department of Computer Science, The Graduate Center, CUNY, USA. Email: grabanca@gmail.com ar X iv :1 60 5. 04 28 4v 1 [ cs .D S] 1 3 M ay 2 01 6

Connected through hyperedge HE-1 (induced by source paper "balancing spreads of influence in a social network"):
  with members:
    - "coverage centrality maximization in undirected networks"
    - "tell me something my friends do not know diversity maximization in social networks"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "maximizing the diversity of exposure in a social network"
    - "balancing information exposure in social networks"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-2 (induced by source paper "iterated two phase local search for the set union knapsack problem"):
  with members:
    - "approximations of the densest k subhypergraph and set union knapsack problems"
    - "a three phase search approach for the quadratic minimum spanning tree problem"

Connected through hyperedge HE-3 (induced by source paper "hypergcn a new method of training graph convolutional networks on hypergraphs"):
  with members:
    - "graph colouring meets deep learning effective graph neural network models for combinatorial problems"
    - "exact k recommendation via maximal clique optimization"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "multi label image recognition with graph convolutional networks"
    - "approximate k cover in hypergraphs efficient algorithms and applications"
    - "planted hitting set recovery in hypergraphs"
    - "a comprehensive survey on graph neural networks"
    - "zero shot recognition via semantic embeddings and knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "an approximation algorithm for active friending in online social networks"):
  with members:
    - "beyond uniform reverse sampling a hybrid sampling technique for misinformation prevention"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "maximizing acceptance probability for active friending in on line social networks"
    - "maximizing social influence in nearly optimal time"
    - "information contagion an empirical study of the spread of news on digg and twitter social networks"

Connected through hyperedge HE-5 (induced by source paper "improved approximation algorithm for the dense 3 subhypergraph problem"):
  with members:
    - "improved approximation algorithms for projection games"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "minimizing the union tight approximations for small set bipartite vertex expansion"
    - "approximation algorithms and hardness of the k route cut problem"
    - "dial a ride from k forest"

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)