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: "predicting human activities using stochastic grammar"
  Abstract: This paper presents a novel method to predict future human activities from partially observed RGB-D videos. Human activity prediction is generally difficult due to its non-Markovian property and the rich context between human and environments. #R##N#We use a stochastic grammar model to capture the compositional structure of events, integrating human actions, objects, and their affordances. We represent the event by a spatial-temporal And-Or graph (ST-AOG). The ST-AOG is composed of a temporal stochastic grammar defined on sub-activities, and spatial graphs representing sub-activities that consist of human actions, objects, and their affordances. Future sub-activities are predicted using the temporal grammar and Earley parsing algorithm. The corresponding action, object, and affordance labels are then inferred accordingly. Extensive experiments are conducted to show the effectiveness of our model on both semantic event parsing and future activity prediction.

Connected through hyperedge HE-1 (induced by source paper "vrgym a virtual testbed for physical and interactive ai"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-2 (induced by source paper "forecasting future sequence of actions to complete an activity"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "forecasting future action sequences with neural memory networks"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "agent centric risk assessment accident anticipation and risky region localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "encouraging lstms to anticipate actions very early"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-3 (induced by source paper "visual affordance and function understanding a survey"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "geometric affordances from a single example via the interaction tensor"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "deep affordance grounded sensorimotor object recognition"
    - "transfer of tool affordance and manipulation cues with 3d vision data"
    - "adaptive binarization for weakly supervised affordance segmentation"
    - "learning to act properly predicting and explaining affordances from images"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-4 (induced by source paper "learning human object interactions by graph parsing neural networks"):
  with members:
    - "situation recognition with graph neural networks"
    - "detecting and recognizing human object interactions"
    - "learning pose grammar to encode human body configuration for 3d pose estimation"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "deformable convolutional networks"
    - "scene graph generation by iterative message passing"
    - "neural message passing for quantum chemistry"
    - "learning to detect human object interactions"

Connected through hyperedge HE-5 (induced by source paper "human centric indoor scene synthesis using stochastic grammar"):
  with members:
    - "intent aware multi agent reinforcement learning"
    - "carla an open urban driving simulator"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "deepcontext context encoding neural pathways for 3d holistic scene understanding"

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)