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: "from statistical knowledge bases to degrees of belief"
  Abstract: An intelligent agent will often be uncertain about various properties of its environment, and when acting in that environment it will frequently need to quantify its uncertainty. For example, if the agent wishes to employ the expected-utility paradigm of decision theory to guide its actions, it will need to assign degrees of belief (subjective probabilities) to various assertions. Of course, these degrees of belief should not be arbitrary, but rather should be based on the information available to the agent. This paper describes one approach for inducing degrees of belief from very rich knowledge bases, that can include information about particular individuals, statistical correlations, physical laws, and default rules. We call our approach the random-worlds method. The method is based on the principle of indifference: it treats all of the worlds the agent considers possible as being equally likely. It is able to integrate qualitative default reasoning with quantitative probabilistic reasoning by providing a language in which both types of information can be easily expressed. Our results show that a number of desiderata that arise in direct inference (reasoning from statistical information to conclusions about individuals) and default reasoning follow directly {from} the semantics of random worlds. For example, random worlds captures important patterns of reasoning such as specificity, inheritance, indifference to irrelevant information, and default assumptions of independence. Furthermore, the expressive power of the language used and the intuitive semantics of random worlds allow the method to deal with problems that are beyond the scope of many other non-deductive reasoning systems.

Connected through hyperedge HE-1 (induced by source paper "polynomial time probabilistic reasoning with partial observations via implicit learning in probability logics"):
  with members:
    - "bayesian estimation from few samples community detection and related problems"
    - "provable learning of noisy or networks"
    - "tensor principal component analysis via sum of squares proofs"
    - "rounding sum of squares relaxations"
    - "hypercontractivity sum of squares proofs and their applications"
    - "characterizing and reasoning about probabilistic and non probabilistic expectation"

Connected through hyperedge HE-2 (induced by source paper "conditional sparse ell_p norm regression with optimal probability"):
  with members:
    - "learning from untrusted data"
    - "conditional sparse linear regression"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-3 (induced by source paper "probabilistic logic programming under inheritance with overriding"):
  with members:
    - "belief functions and default reasoning"
    - "generating bayesian networks from probability logic knowledge bases"
    - "credal networks under maximum entropy"
    - "relational bayesian networks"
    - "hybrid probabilistic programs algorithms and complexity"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"
    - "probabilistic default reasoning with conditional constraints"

Connected through hyperedge HE-4 (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"
    - "probabilistic default reasoning with conditional constraints"
    - "probabilistic deduction with conditional constraints over basic events"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-5 (induced by source paper "defining explanation in probabilistic systems"):
  with members:
    - "causality in bayesian belief networks"
    - "counterfactual probabilities computational methods bounds and applications"
    - "relevant explanations allowing disjunctive assignments"
    - "generating bayesian networks from probability logic knowledge bases"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-6 (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"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-7 (induced by source paper "entailment in probability of thresholded generalizations"):
  with members:
    - "reasoning with qualitative probabilities can be tractable"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-8 (induced by source paper "belief change with noisy sensing in the situation calculus"):
  with members:
    - "reasoning about noisy sensors and effectors in the situation calculus"

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)