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: "random worlds and maximum entropy"
  Abstract: Given a knowledge base KB containing first-order and statistical facts, we consider a principled method, called the random-worlds method, for computing a degree of belief that some formula Phi holds given KB. If we are reasoning about a world or system consisting of N individuals, then we can consider all possible worlds, or first-order models, with domain {1,...,N} that satisfy KB, and compute the fraction of them in which Phi is true. We define the degree of belief to be the asymptotic value of this fraction as N grows large. We show that when the vocabulary underlying Phi and KB uses constants and unary predicates only, we can naturally associate an entropy with each world. As N grows larger, there are many more worlds with higher entropy. Therefore, we can use a maximum-entropy computation to compute the degree of belief. This result is in a similar spirit to previous work in physics and artificial intelligence, but is far more general. Of equal interest to the result itself are the limitations on its scope. Most importantly, the restriction to unary predicates seems necessary. Although the random-worlds method makes sense in general, the connection to maximum entropy seems to disappear in the non-unary case. These observations suggest unexpected limitations to the applicability of maximum-entropy methods.

Connected through hyperedge HE-1 (induced by source paper "credal networks under maximum entropy"):
  with members:
    - "coherent knowledge processing at maximum entropy by spirit"
    - "generating bayesian networks from probability logic knowledge bases"
    - "decision making with interval influence diagrams"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "from statistical knowledge bases to degrees of belief"
    - "probabilistic default reasoning with conditional constraints"
    - "probabilistic deduction with conditional constraints over basic events"

Connected through hyperedge HE-2 (induced by source paper "reasoning under the principle of maximum entropy for modal logics k45 kd45 and s5"):
  with members:
    - "formula based probabilistic inference"
    - "counting belief propagation"
    - "from statistical knowledge bases to degrees of belief"

Connected through hyperedge HE-3 (induced by source paper "generating new beliefs from old"):
  with members:
    - "relative entropy probabilistic inference and ai"
    - "a logic for default reasoning about probabilities"

Connected through hyperedge HE-4 (induced by source paper "entailment in probability of thresholded generalizations"):
  with members:
    - "reasoning with qualitative probabilities can be tractable"
    - "from statistical knowledge bases to degrees of belief"

Connected through hyperedge HE-5 (induced by source paper "a logic for default reasoning about probabilities"):
  with members:
    - "relative entropy probabilistic inference and ai"

Connected through hyperedge HE-6 (induced by source paper "an equilibrium analysis of scrip systems"):
  with members:
    - "optimizing scrip systems crashes altruists hoarders sybils and collusion"
    - "mean field equilibrium in dynamic games with complementarities"
    - "manipulating scrip systems sybils and collusion"
    - "optimizing scrip systems efficiency crashes hoarders and altruists"
    - "efficiency and nash equilibria in a scrip system for p2p networks"

Connected through hyperedge HE-7 (induced by source paper "temporal probabilistic logic programs state and revision"):
  with members:
    - "hybrid probabilistic programs algorithms and complexity"

Connected through hyperedge HE-8 (induced by source paper "manipulating scrip systems sybils and collusion"):
  with members:
    - "optimizing scrip systems efficiency crashes hoarders and altruists"
    - "efficiency and nash equilibria in a scrip system for p2p networks"

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.AI(Artificial Intelligence)