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: "definition and properties to assess multi agent environments as social intelligence tests"
  Abstract: Social intelligence in natural and artificial systems is usually measured by the evaluation of associated traits or tasks that are deemed to represent some facets of social behaviour. The amalgamation of these traits is then used to configure the intuitive notion of social intelligence. Instead, in this paper we start from a parametrised definition of social intelligence as the expected performance in a set of environments with several agents, and we assess and derive tests from it. This definition makes several dependencies explicit: (1) the definition depends on the choice (and weight) of environments and agents, (2) the definition may include both competitive and cooperative behaviours depending on how agents and rewards are arranged into teams, (3) the definition mostly depends on the abilities of other agents, and (4) the actual difference between social intelligence and general intelligence (or other abilities) depends on these choices. As a result, we address the problem of converting this definition into a more precise one where some fundamental properties ensuring social behaviour (such as action and reward dependency and anticipation on competitive/cooperative behaviours) are met as well as some other more instrumental properties (such as secernment, boundedness, symmetry, validity, reliability, efficiency), which are convenient to convert the definition into a practical test. From the definition and the formalised properties, we take a look at several representative multi-agent environments, tests and games to see whether they meet these properties.

Connected through hyperedge HE-1 (induced by source paper "universal psychometrics tasks difficulty composition and decomposition"):
  with members:
    - "ai evaluation past present and future"
    - "an approximation of the universal intelligence measure"
    - "a monte carlo aixi approximation"
    - "normalized information distance"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-2 (induced by source paper "ai evaluation past present and future"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "no free lunch versus occam s razor in supervised learning"
    - "an approximation of the universal intelligence measure"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "graphics turing test"
    - "the 3rd international planning competition results and analysis"

Connected through hyperedge HE-3 (induced by source paper "a note about the generalisation of the c tests"):
  with members:
    - "universal psychometrics tasks difficulty composition and decomposition"
    - "ai evaluation past present and future"
    - "the arcade learning environment an evaluation platform for general agents"
    - "an approximation of the universal intelligence measure"
    - "tests of machine intelligence"
    - "universal intelligence a definition of machine intelligence"

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.MA(Multiagent Systems)