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: "almost polynomial ratio eth hardness of approximating densest k subgraph"
  Abstract: In the Densest $k$-Subgraph problem, given an undirected graph $G$ and an integer $k$, the goal is to find a subgraph of $G$ on $k$ vertices that contains maximum number of edges. Even though the state-of-the-art algorithm for the problem achieves only $O(n^{1/4 + \varepsilon})$ approximation ratio (Bhaskara et al., 2010), previous attempts at proving hardness of approximation, including those under average case assumptions, fail to achieve a polynomial ratio; the best ratios ruled out under any worst case assumption and any average case assumption are only any constant (Raghavendra and Steurer, 2010) and $2^{\Omega(\log^{2/3} n)}$ (Alon et al., 2011) respectively. #R##N#In this work, we show, assuming the exponential time hypothesis (ETH), that there is no polynomial-time algorithm that approximates Densest $k$-Subgraph to within $n^{1/(\log \log n)^c}$ factor of the optimum, where $c > 0$ is a universal constant independent of $n$. In addition, our result has "perfect completeness", meaning that we prove that it is ETH-hard to even distinguish between the case in which $G$ contains a $k$-clique and the case in which every induced $k$-subgraph of $G$ has density at most $1/n^{-1/(\log \log n)^c}$ in polynomial time. #R##N#Moreover, if we make a stronger assumption that there is some constant $\varepsilon > 0$ such that no subexponential-time algorithm can distinguish between a satisfiable 3SAT formula and one which is only $(1 - \varepsilon)$-satisfiable (also known as Gap-ETH), then the ratio above can be improved to $n^{f(n)}$ for any function $f$ whose limit is zero as $n$ goes to infinity (i.e. $f \in o(1)$).

Connected through hyperedge HE-1 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "from group recommendations to group formation"
    - "when social influence meets item inference"

Connected through hyperedge HE-2 (induced by source paper "parameterized inapproximability for steiner orientation by gap amplification"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "constant factor fpt approximation for capacitated k median"
    - "faster exact and approximate algorithms for k cut"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "completely inapproximable monotone and antimonotone parameterized problems"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "directed multicut is w 1 hard even for four terminal pairs"
    - "the parameterized complexity of k biclique"

Connected through hyperedge HE-3 (induced by source paper "election manipulation on social networks seeding edge removal edge addition"):
  with members:
    - "election control in social networks via edge addition or removal"
    - "controlling elections through social influence"
    - "minority becomes majority in social networks"
    - "maximizing the spread of cascades using network design"
    - "on budgeted influence maximization in social networks"

Connected through hyperedge HE-4 (induced by source paper "adaptive sequence submodularity"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-5 (induced by source paper "principal fairness removing bias via projections"):
  with members:
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair clustering through fairlets"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "active fairness in algorithmic decision making"
    - "inapproximability of maximum biclique problems minimum k cut and densest at least k subgraph from the small set expansion hypothesis"
    - "ranking with fairness constraints"

Connected through hyperedge HE-6 (induced by source paper "on multi cascade influence maximization model hardness and algorithmic framework"):
  with members:
    - "on misinformation containment in online social networks"
    - "balancing information exposure in social networks"
    - "distributed rumor blocking with multiple positive cascades"
    - "an efficient randomized algorithm for rumor blocking in online social networks"
    - "stop and stare optimal sampling algorithms for viral marketing in billion scale networks"
    - "correlated cascades compete or cooperate"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "influence maximization near optimal time complexity meets practical efficiency"

Connected through hyperedge HE-7 (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"
    - "the densest k subhypergraph problem"
    - "from competition to complementarity comparative influence diffusion and maximization"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-8 (induced by source paper "election control with voters uncertainty hardness and approximation results"):
  with members:
    - "vote for me election control via social influence in arbitrary scoring rule voting systems"
    - "election campaigning on social media politicians audiences and the mediation of political communication on facebook and twitter"
    - "election with bribed voter uncertainty hardness and approximation algorithm"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "controlling elections through social influence"
    - "complexity of shift bribery in committee elections"
    - "minority becomes majority in social networks"
    - "determining possible and necessary winners under common voting rules given partial orders"

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.CC(Computational Complexity)