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: "in defense of the direct perception of affordances"
  Abstract: The field of functional recognition or affordance estimation from images has seen a revival in recent years. As originally proposed by Gibson, the affordances of a scene were directly perceived from the ambient light: in other words, functional properties like sittable were estimated directly from incoming pixels. Recent work, however, has taken a mediated approach in which affordances are derived by first estimating semantics or geometry and then reasoning about the affordances. In a tribute to Gibson, this paper explores his theory of affordances as originally proposed. We propose two approaches for direct perception of affordances and show that they obtain good results and can out-perform mediated approaches. We hope this paper can rekindle discussion around direct perception and its implications in the long term.

Connected through hyperedge HE-1 (induced by source paper "putting humans in a scene learning affordance in 3d indoor environments"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "binge watching scaling affordance learning from sitcoms"
    - "virtualhome simulating household activities via programs"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "context aware synthesis and placement of object instances"
    - "unified perceptual parsing for scene understanding"
    - "numerical coordinate regression with convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "binge watching scaling affordance learning from sitcoms"):
  with members:
    - "learning a predictable and generative vector representation for objects"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "the curious robot learning visual representations via physical interactions"
    - "single image 3d interpreter network"
    - "what happens if learning to predict the effect of forces in images"
    - "r fcn object detection via region based fully convolutional 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.CV(Computer Vision and Pattern Recognition)