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: "when the map is better than the territory"
  Abstract: The causal structure of any system can be analyzed at a multitude of spatial and temporal scales. It has long been thought that while higher scale (macro) descriptions may be useful to observers, they are at best a compressed description and at worse leave out critical information and causal relationships. However, recent research applying information theory to causal analysis has shown that the causal structure of some systems can actually come into focus and be more informative at a macroscale. That is, a macroscale description of a system (a map) can be more informative than a fully detailed microscale description of the system (the territory). This has been called “causal emergence.” While causal emergence may at first seem counterintuitive, this paper grounds the phenomenon in a classic concept from information theory: Shannon’s discovery of the channel capacity. I argue that systems have a particular causal capacity, and that different descriptions of those systems take advantage of that capacity to various degrees. For some systems, only macroscale descriptions use the full causal capacity. These macroscales can either be coarse-grains, or may leave variables and states out of the model (exogenous, or “black boxed”) in various ways, which can improve the efficacy and informativeness via the same mathematical principles of how error-correcting codes take advantage of an information channel’s capacity. The causal capacity of a system can approach the channel capacity as more and different kinds of macroscales are considered. Ultimately, this provides a general framework for understanding how the causal structure of some systems cannot be fully captured by even the most detailed microscale description.

Connected through hyperedge HE-1 (induced by source paper "the journey is the reward unsupervised learning of influential trajectories"):
  with members:
    - "diversity is all you need learning skills without a reward function"
    - "a unified bellman equation for causal information and value in markov decision processes"
    - "proximal policy optimization algorithms"
    - "variational intrinsic control"
    - "empowerment an introduction"
    - "empowerment for continuous agent environment systems"

Connected through hyperedge HE-2 (induced by source paper "finding the right scale of a network efficient identification of causal emergence through spectral clustering"):
  with members:
    - "representing higher order dependencies in networks"
    - "an overview of gradient descent optimization algorithms"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-3 (induced by source paper "relating complexities for the reflexive study of complex systems"):
  with members:
    - "self referential basis of undecidable dynamics from the liar paradox and the halting problem to the edge of chaos"
    - "a spatialised bibliometrics approach of a scientific journal production"
    - "an applied knowledge framework to study complex systems"
    - "exploration of an interdisciplinary scientific landscape"
    - "the generalized traveling salesman problem solved with ant algorithms"
    - "a hybrid network grid model of urban morphogenesis and optimization"
    - "complexity measurement of natural and artificial languages"
    - "the world as evolving information"

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.IT(Information Theory)