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: "achieving target equilibria in network routing games without knowing the latency functions"
  Abstract: The analysis of network routing games typically assumes, right at the onset, precise and detailed information about the latency functions. Such information may, however, be unavailable or difficult to obtain. Moreover, one is often primarily interested in enforcing a desired target flow as the equilibrium by suitably influencing player behavior in the routing game. We ask whether one can achieve target flows as equilibria without knowing the underlying latency functions. #R##N#Our main result gives a crisp positive answer to this question. We show that, under fairly general settings, one can efficiently compute edge tolls that induce a given target multicommodity flow in a nonatomic routing game using a polynomial number of queries to an oracle that takes candidate tolls as input and returns the resulting equilibrium flow. This result is obtained via a novel application of the ellipsoid method. Our algorithm extends easily to many other settings, such as (i) when certain edges cannot be tolled or there is an upper bound on the total toll paid by a user, and (ii) general nonatomic congestion games. We obtain tighter bounds on the query complexity for series-parallel networks, and single-commodity routing games with linear latency functions, and complement these with a query-complexity lower bound. We also obtain strong positive results for Stackelberg routing to achieve target equilibria in series-parallel graphs. #R##N#Our results build upon various new techniques that we develop pertaining to the computation of, and connections between, different notions of approximate equilibrium; properties of multicommodity flows and tolls in series-parallel graphs; and sensitivity of equilibrium flow with respect to tolls. Our results demonstrate that one can indeed circumvent the potentially-onerous task of modeling latency functions, and yet obtain meaningful results for the underlying routing game.

Connected through hyperedge HE-1 (induced by source paper "can taxes improve congestion on all networks"):
  with members:
    - "path deviations outperform approximate stability in heterogeneous congestion games"
    - "asymptotically tight bounds for inefficiency in risk averse selfish routing"
    - "playing the wrong game smoothness bounds for congestion games with behavioral biases"
    - "potential games are necessary to ensure pure nash equilibria in cost sharing games"

Connected through hyperedge HE-2 (induced by source paper "online constraint satisfaction via tolls in mdp congestion games"):
  with members:
    - "tolling for constraint satisfaction in markov decision process congestion games"
    - "adaptive incentive design"
    - "watch and learn optimizing from revealed preferences feedback"

Connected through hyperedge HE-3 (induced by source paper "recovering games from perturbed equilibrium observations using convex optimization"):
  with members:
    - "inducing approximately optimal flow using truthful mediators"
    - "econometrics for learning agents"
    - "the empirical implications of rank in bimatrix games"

Connected through hyperedge HE-4 (induced by source paper "watch and learn optimizing from revealed preferences feedback"):
  with members:
    - "the sample complexity of revenue maximization"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "the pseudo dimension of near optimal auctions"
    - "learning economic parameters from revealed preferences"
    - "sampling and representation complexity of revenue maximization"
    - "online learning and profit maximization from revealed preferences"
    - "making the most of your samples"
    - "mechanism design for data science"

Connected through hyperedge HE-5 (induced by source paper "inducing approximately optimal flow using truthful mediators"):
  with members:
    - "private matchings and allocations"
    - "jointly private convex programming"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "privacy and truthful equilibrium selection for aggregative games"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "calibrating data to sensitivity in private data analysis"
    - "mechanism design in large games incentives and privacy"
    - "approximately optimal mechanism design via differential privacy"

Connected through hyperedge HE-6 (induced by source paper "learning from rational behavior predicting solutions to unknown linear programs"):
  with members:
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "optimal learners for multiclass problems"
    - "learning what s going on reconstructing preferences and priorities from opaque transactions"
    - "efficiently learning from revealed preference"

Connected through hyperedge HE-7 (induced by source paper "computing optimal tolls in routing games without knowing the latency functions"):
  with members:
    - "watch and learn optimizing from revealed preferences feedback"

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.GT(Computer Science and Game Theory)