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: "general truthfulness characterizations via convex analysis"
  Abstract: We present a model of truthful elicitation which generalizes and extends mechanisms, scoring rules, and a number of related settings that do not quite qualify as one or the other. Our main result is a characterization theorem, yielding characterizations for all of these settings, including a new characterization of scoring rules for non-convex sets of distributions. We generalize this model to eliciting some property of the agent's private information, and provide the first general characterization for this setting. We also show how this yields a new proof of a result in mechanism design due to Saks and Yu.

Connected through hyperedge HE-1 (induced by source paper "the complexity of partial function extension for coverage functions"):
  with members:
    - "partial function extension with applications to learning and property testing"
    - "the limitations of optimization from samples"
    - "maximizing social influence in nearly optimal time"
    - "privately releasing conjunctions and the statistical query barrier"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-2 (induced by source paper "an embedding framework for consistent polyhedral surrogates"):
  with members:
    - "on the consistency of top k surrogate losses"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "analysis and optimization of loss functions for multiclass top k and multilabel classification"
    - "convex calibration dimension for multiclass loss matrices"
    - "learning with submodular functions a convex optimization perspective"

Connected through hyperedge HE-3 (induced by source paper "multi observation elicitation"):
  with members:
    - "learning deep embeddings with histogram loss"
    - "elicitation complexity of statistical properties"
    - "facenet a unified embedding for face recognition and clustering"
    - "elicitation for aggregation"

Connected through hyperedge HE-4 (induced by source paper "optimal auctions with restricted allocations"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "bounding the optimal revenue of selling multiple goods"
    - "selling two goods optimally"
    - "duality and optimality of auctions for uniform distributions"
    - "strong duality for a multiple good monopolist"
    - "mechanism design via optimal transport"
    - "the simple economics of approximately optimal auctions"

Connected through hyperedge HE-5 (induced by source paper "truthful multi parameter auctions with online supply an impossible combination"):
  with members:
    - "the power of randomness in bayesian optimal mechanism design"
    - "auctions with online supply"
    - "online stochastic matching beating 1 1 e"

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)