human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "multiparty dynamics and failure modes for machine learning and artificial intelligence"):
  Abstract: An important challenge for safety in machine learning and artificial intelligence systems is a~set of related failures involving specification gaming, reward hacking, fragility to distributional shifts, and Goodhart's or Campbell's law. This paper presents additional failure modes for interactions within multi-agent systems that are closely related. These multi-agent failure modes are more complex, more problematic, and less well understood than the single-agent case, and are also already occurring, largely unnoticed. After motivating the discussion with examples from poker-playing artificial intelligence (AI), the paper explains why these failure modes are in some senses unavoidable. Following this, the paper categorizes failure modes, provides definitions, and cites examples for each of the modes: accidental steering, coordination failures, adversarial misalignment, input spoofing and filtering, and goal co-option or direct hacking. The paper then discusses how extant literature on multi-agent AI fails to address these failure modes, and identifies work which may be useful for the mitigation of these failure modes.

Includes members:
  Member "autocurricula and the emergence of innovation from social interaction a manifesto for multi agent intelligence research":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "silly rules improve the capacity of agents to learn stable enforcement and compliance behaviors")
  Member "oversight of unsafe systems via dynamic safety envelopes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overoptimization failures and specification gaming in multi agent systems")
  Member "how do classifiers induce agents to invest effort strategically":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "data efficient deep reinforcement learning for dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning precise 3d manipulation from multiple uncalibrated cameras")
  Member "supervising strong learners by amplifying weak experts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effective reinforcement learning through evolutionary surrogate assisted prescription")
  Member "robustness to fundamental uncertainty in agi alignment":
    also connected through other hyperedges:
      (none)
  Member "data poisoning attacks against online learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on defending against label flipping attacks on malware detection systems")

Question: Please tell me which class the source paper 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)